DAA: Amplifying Unknown Discrepancy for Test-Time Discovery
Authors: Tianle Liu
Fan Lyu
Chenggong Ni
Zhang Zhang
Fuyuan Hu
Liang Wang
Venue: NeurIPS 2025 poster ()
Keywords: Test-Time Discovery
Novel Category Discovery
Online Learning
Test-Time Training
Primary area: general_machine_learning
TL;DR: We propose DAA, a trainable module that enables real-time adaptation by amplifying feature-level discrepancies between known and unknown classes in TTD.

Abstract: Test-Time Discovery (TTD) addresses the critical challenge of identifying and adapting to novel classes during inference while maintaining performance on known classes, which is a capability essential for dynamic real-world environments such as healthcare and autonomous driving. Recent TTD methods adopt training-free, memory-based strategies but rely on frozen models and static representations, resulting in poor generalization. In this paper, we propose a Discrepancy-Amplifying Adapter (DAA), a trainable module that enables real-time adaptation by amplifying feature-level discrepancies between known and unknown classes. During training, DAA is optimized using simulated unknowns and a novel warm-up strategy to enhance its discriminative capacity. To ensure continual adaptation at test time, we introduce a Short-Term Memory Renewal (STMR) mechanism, which maintains a queue-based memory for unknown classes and selectively refreshes prototypes using recent, reliable samples. DAA is further updated through self-supervised learning, promoting knowledge retention for known classes while improving discrimination of emerging categories. Extensive experiments show that our method maintains high adaptability and stability, and significantly improves novel class discovery performance. Our code will be available.

DAA: Amplifying Unknown Discrepancy for Test-Time Discovery
Tianle Liu! Fan Lyu"), Chenggong Ni', Zhang Zhang', Fuyuan Hu" 1,5 Liang Wang' School of Electronics and Information Engineering, Suzhou University of Science and Technology Suzhou Key Laboratory of Embodied Intelligent Agents for Cooperative Perception and Advanced Control
New Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
*Jiangsu Industrial Intelligent and Low-carbon Technology Engineering Center Suzhou Key Laboratory of Intelligent and Low-carbon Technology Application {tianleliu, cgn}&post. usts. edu.cn fan. Jan.lyucrripce ia. ac cn fuyuanhu@mail usts..ss..... edu. cn
Abstract
Test-Time Discovery (TTD) addresses the critical challenge of identifying and adapting to novel classes during inference while maintaining performance on known classes, which a capability essential for dynamic real-world environ- ments such as healthcare and autonomous driving. Recent TTD methods adopt taining-frree, memory-baste bassed strategies but rely on frozen models and static repre- sentations, resulting in poor generalization. In this paper, propose Discrepancy- Amplifying Adapter (DAA), trainable module that enables real-time adaptation by amplifying feature-level discrepancies between known and unknown classes. During training, DAA optimized using simulated unknowns and novel warm- up strategy to enhance its discriminative capacity. To ensure continual adapta- tion at test time, we introduce a Short-Term Memory Renewal (STMR) mech- anism, which maintains a queue-based memory for unknown classes and selec- tively refreshes prototypes using recent, reliable samples. DAA is further up- dated through self-supervised learning, promoting knowledge retention for known classes while improving discrimination of emerging categories. Extensive ex- periments show that our method maintains high adaptability and stability, and significantly improves novel class discovery performance. Our code is available at https:://e //git//thtt www.LeeianLn-nL-eeeeeee. -TAAA--foo
1 Introduction
Test-Time Discovery (TTD) 22 is an emerging and increasingly important that aims to dynami- cally identify and classify novel categories during the test phase, while simultaneously maintaining robust performance on previously learned classes. This capability is critical for real-world applica- tions such healthcare, autonomous driving, and robotics, where models must adapt to previously
unseen classes after deployment. While Test-Time Adaptation (TTA) [36] 32|
7 has attracted
substantial attention for mitigating domain shifts, it typically overlooks the challenge of class shifts,
33 is designed for static settings with separated labeled and unlabeled data, and falls short in test-time scenarios, thus generally relies on offline clustering and post-hoc evaluation, assuming that novel categories are only identified in controlled training environment. Unlike TTA and NCD, TTD explicitly targets novel class discovery during inference, operates in a distinct paradigm, requiring real-time adaptation
particularly the emergence new categories. Novel Class Discovery (NCD) 22
"Coreesoodiig authors.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Training Phase
Testing Phase
+ Classcs
+
Labelled Training Set
Unlabelled lesting Set
Cloors
Data
Dosg
Cal
Medel
Known Classes
Known Classes
Unknown Classes
Difficult discover
Confused
Recent Training-Free Method (HM)
Train
boundary
Model
Backbone
Backbone
DAA
TTT
Warm-up
Predict
Taning
Backbone
DAA
Known
Unknown
Easier to discover
Amplified
Discrepancy
Which class ? Dog/ New8/1 NewH2
Ours
(a) TTD with adapter
(h) Training-free method vs. method
Figure 1: (a) In Test-Time Discovery (TTD), model is pretrained on data containing only known classes. During deployment, the test data may include both known and unknown classes, requiring the model to predict known classes and discover novel ones. (b) Recent training- free method [22] relies on fixed features and is difficult classify via prototype only. Our method maintains known classes and amplifies differences of unknown classes.
to continuous unlabeled streams of both known and unknown classes, enabling models to maintain performance and learn emerging categories.
Recent advances in TTD 22 propose training free, hash-based memory mechanism for fine-grained comparisons with past samples, enabling novel class discovery during inference. While eliminating the need for model update, this approach relies frozen model parameters and updates only an external memory. As a result, it suffers from significant estimation errors and limited generalization. As shown in Fig. [b the use of static prototypes constrains novel classes to the existing feature space of known classes, often leading to feature overlap and misclassification.
In this paper, we propose a Sescrepancc-Ampliyyyie Adapter (DAA) for the TTD task. Unlike prior methods based on frozen architectures, DAA is a trainable module that enables effective adaptation to novel knowledge. During training. the backbone learns known classes, while DAA is optimized to preserve feature consistency. To enhance discrimination of unknown classes, DAA trained with simulated unknowns, encouraging amplified feature discrepancies by a warm-up strategy. At test time, we introduce a novel Short-Term Term Memory Renewal (STMR) strategy, inspired by the memory strategy in the previous method [22]. to mitigate misclassification. STMR employs queue- gusee- memory to prioritize recent and reliable samples, supporting prototype updates, sample replay, and test-time refinement of DAA. This design improves both memory efficiency and model adaptability. DAA adapts in real-time testing through self-supervised updates based on predictions and the memory queue, which ensures knowledge alignment for known classes and improves model plasticity and generalization to novel classes. Experimental results show that our method outperforms existing state-of-the teff-hheand art methods with higher adaptability and stability, and significantly improves novel class discovery performance.
In summary, our contributions are:
(1) Unlike existing training-free methods that rely solely on pre-trained models, we propose trainable module DAA, to address the TTD problem by amplifying feature differences.
(2) We propose STMR strategy, which rectifies outdated and unreliable knowledge and is used to maintain known classes and improve the discrimination unknown classes.
(3) Extensive experiments demonstrate that our method significantly outperforms existing methods.
2 Related Work
Test- Time Training (TTT) 16 30 21 is an emerging paradigm in machine learning that focuses on adapting pre-trained models distribution shifts during inference. Unlike traditional training methods that rely solely on fixed dataset, TTT leverages self-supervised tasks to update model parameters

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dynamically using the input data structure. TTT methods such as TTT+ 120 and TTT-MAE 9 employ auxiliary tasks like image rotation prediction or masked autoencoding to refine feature extractors during inference. These methods aim to align the model's internal representations with the test data distribution, thereby enhancing generalization [7 31. However, TTT methods typically focus on predefined categories and overlook the discovery of novel classes during testing, which is a critical limitation in open-world environments where new classes may emerge unexpectedly.
45 addresses the challenge of identifying previously unseen classes during inference. NCD methods can detect new categories autonomously, often through clustering techniques. For instance, DTC [T]] leverages prior knowledge of related image classes to reduce ambiguity in clustering and enhance the quality of newly discovered classes. Other
Novel Category Discovery (NCD) TO
methods, such as PrompiCCD [3] and GCD [3], employ advanced techniques to improve the detection and representation of novel classes. On-the-fly Category Discovery (OCD) B 44 tries to perform online discovery and make instant inference by hash coding and hamming distance. However, OCD focuses on discovery rather than improving performance through the testing phase and can only provide hash descriptions for class prototypes, which ignore the model adaptation new classes. While NCD has made significant progress in offline settings, it falls short in dynamic, real-time applications where models must adapt to new classes instantly.
Test-Time Discovery (TTD) introduces a novel task that enables models to dynamically discover novel classes during inference while maintaining known-class performance, bridging the gap between TTT and NCD. The pioneering work by Lyu et al. [22]] proposes a training-free framework leveraging Locality-Sensitive Hashing (L.SH) to construct memory buffer for efficient sample comparison and pseudo-label refinement. Their method groups similar test samples, combining global prototypes and local hash-based predictions. While this approach mitigates catastrophic forgetting by freezing model parameters, it inherently limits the model's capacity to adaptively refine feature representations for enhanced discrimination between old and new classes. Recent efforts in Test-Time Adaptation (TTA) II 4 225 explore lightweight parameter updates but remain confined to domain shifts rather than class shifts. Our method aims to address the limitations of existing TTD techniques by enhancing the model's 's ability to adapt to new classes in real-time while maintaining robust performance on known classes, critical improvement over static architectures in evolving open-world settings.
3 Method: Discrepancy-Amplliiinn Adapter
3.1 Problem Definition
The goal of TTD is to enable testing models to not only classify samples from known classes accurately, but also to identify and classify samples from unknown classes dynamically by adaptation. During the training phuse, let Dirain = ((1,, mx {x))) ).Muuj be the training dataset, where is data point and yi E Yan is its corresponding label from the set of known classes Vkn The model f is trained on Durin to learn the representations of the known classes. During the test phase, the model encounters test dataset Drest = Din U Dun The set of unknown classes comprises seen and unseen parts, i.c., Vm = Vesen U Once new class is discovered, it becomes seen class. As the example shown in Fig. Ia after discovering new classes "Apple" and "Bird", a TTD model needs to distinguish whether the test sample is "Dog", "Cat", "Apple", or "Bird".
The TTD task requires the model achieve two objectives. First, novel class discovery and learning. where inputs E Dus must be identified as belonging to unseen classes and assigned new labels to distinguish them from known-class samples. Second, unified classification, where all inputs x E Drest are accurately classified into either known class y Yan or novel class y E Yeeems with Veeen denoting the set of dynamically discovered novel classes.
Existing TTD methods (22) follow the NCD paradigm, which relies on fixed backbone where feature representations for unknown classes remain static during the testing phase. These approaches avoid model updates and instead depend on memory buffers storing representative samples, with prediction and novel class discovery based solely on distances to stored prototypes. However, the frozen representations hinder the model's emerging unknown classes. To overcome this limitation, we introduce a trainable component, the Discrepancy-A Amplnnyiiiiiiiiiggggggg. Adapter (DAA). Moreover, to improve adaptability and ensure long-term efficacy, we propose Short-Term Memory Renewal (STMR), a dynamic memory mechanism that continuously updates and refreshes stored representations.
's ability to effectively discriminate between known and
3

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Training Phase
MSE
Testing Phase
MSK
Backhone
Backbone
Known classess
Known closses
Known classes
Replay samples
Knewn classes
DAA
Test samples
Backkbome
DAA
Prodiction
Whenh cloi (UAIER
simulate
Renewal samples
Known
Unknown classes
Unknown classes
classes
Contrustive Loss
Contrastive
Sunple
Replay samples
Feature Queue
Renewal samples
Unkaown
Sample Quese
Initiaizee
Unknown classes
(Karwa ciasc)
Calculate Prototype
Figure 2: Method Schema. The DAA preserves known class features and amplifies discrepancies with unknowns-simulace during training and encountered during testing. It is updated online to maintain discrimination. The memory system includes sample queues for replay and renewal, and feature queues for prototype computation.
3.2
Discrepancy-Ampliyiing Warm-up before Testing
The failure of past TTD methods was because unknown classes and known classes could not be effectively separated, and this situation could not be improved over time. DAA is lightweight module inserted into pre-trained model to enable efficient fine-tuning for unknown classes. Given an input image æ, the backbone produces feature vector r(z) f(x) IR which is then transformed by DAA into an adapted representation. The core design of DAA lies in keeping the backbone that contains known classes unchanged, and entrusting the discovery and learning of new classes to
the constructed DAA module. To achieve this, we first propose strategy for DAA update before the testing phase. Warm-up has been used in TTA (26 41 further build a knowledge structure for the source model P 5 The discrepancy-ampliifiin warm-up strategy first simulates unknown classes and then trains with pre-amplify loss.
cerrepancc-amllffyee
warm-up
Unknown Class Simulation. During training, only known classes are available, yet the model remain adaptable future unknowns. To address this, in the discrepancy-amplify.
warm-up strategy, we propose to simulate unknown class features by combining Gaussian noise and the Mixup technique (4). Given two difference samples and x', the synthetic feature F generated follows:
F(z) À r(x) + - A) r))))+(++++)))))))) +n,
(1)
where n denotes a vector Gaussian noise with mean of zero and certain covariance matrix. The mixing coefficient À is drawn from a Beta distribution. This augmentation increases training diversity and encourages the DAA learn more generalized, transferable representations.
Discrepancy Pre-Ampiification. Building upon the unknown class simulation introduced in the warm-up stage, design dual-objective training to guide the discrepancy pre-amplification of DAA. The effect the training must retain the original representation of known classes while amplifying the distinction between simulated unknown and known features. To preserve the learned semantics of known classes, we constrain the DAA output to remain close to the original backbone feature f(x) using an MSE loss. Simultaneously, given a test batch B, and for æ E B, we apply a contrastive loss on the adapted unknown features to maximize inter-class discrepancy, ensuring the model learns more discriminative representations for novel classes:
exp(o(DAA(F(x))) (AA(F(x))
(xx ||| [DAA(r(z)) - rr=))] Lmn(x) log Σn Dx'eB expla (DAA((()AA DAA (2)
where a denotes the cosine similarity. DAA(F(z))aug is the feature obtained through data augmenta- tion operations. The total warm-up loss for DAA is a weighted combination:
Cirin ( E,es t (z) + (z).
This Easerepancy-Ampliyyiie Warm-up strategy has been empirically shown to facilitate novel class discovery by enhancing feature separation during the early training phase. However, during testing, samples from unknown classes often exhibit high uncertainty and noise. Blindly updating the model with such unreliable samples can undermine the benefits of warm-up. potentially causing semantic drift and loss of discrimination between known and unknown classes.

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3.3 Short-Term Memory Renewal
Inspired by we introduce Short-Temn Memory Renewal (STMR) mechanism during testing. This memory-guides strategy enables the DAA to selectively integrate informative samples while continuously refreshing its representation space, thereby preserving class boundaries and stabilizing adaptation in the presence of noisy unknown inputs. As illustrated in Fig. 3 STMR employs a queue- based memory system consisting of sample queues and feature queues F for each class. For known classes, memory queues are initialized using training data and remain static. Their corresponding prototypes are computed and fixed during the training phase. To prevent catastrophic forgetting, employ sample replay by randomly selecting "i ~ St and using them to regularize the DAA updates for known class c E Van In contrast, novel classes begin with empty memory, which is updated dynamically new unknown samples are discovered. Unknown-class memory Sun and Fun follows a First-In- First-Our (FIFO) strategy to ensure temporal relevance.
However, since the initial predictions for unknown samples may be noisy and DAA parameters evolve over time, prototype drift and representation mismatch may occur. To address this, STMR introduces a renewal step and it will trigger every several batch. A subset of unknown memory samples x ~ S are extracted by the backbone and DAA for a seen class c. Then, the feature f = DAA(r(z)) is re-evaluated to obtain updated predictions ÿ. If E Va. the sample is discarded to avoid contamination of unknown memory with misclassified known-class data. If ÿ E Yan. the feature f is used to update DAA via contrastive loss to enhance its separability from known-class features. Then r and re-quees re-queue the sample and its updated feature into Sg and Fg respectively. The update of the unknown sample and feature queues updated as follows:
S un
FIFO x),
FC=Ú
FIFO un
,t).
The prediction and test-time training using DAA can be seen in Sec. 3.4
For a newly discovered class c, the prototype is computed as the mean of its feature queue:
P. = Exes, (DAA(r(z))).
(5)
Compared with the memory strategy in HM [22], which stores uncertain samples over time but does not update the model, STMR uses short-term, renewable memory with selective filtering to retain only reliable representations. This design not only avoids the accumulation of errors but also enables model updates via contrastive learning, allowing STMR to adapt to novel classes while preserving known-class performance.
3.4 Test-Time Prediction and Training with DAA
In this subsection, we illustrate how DAA to conduct prediction and learning test time. When encountering the test data point with known or unknown class, the prediction procedure involves caleulating the cosine similarity between the test samples and the prototypes of known classes to determine their classification. Based on the max similarity scores, we apply a confidence threshold to determine whether test sample x belongs to seen class or unseen class.
g arg max.eeQLuu.... (P(:)), if max. P(x) 7
(6)
new unseen class,
otherwise,
where prediction comparison P(r)) = sim (im(DAA(r(=))) Pc), sim() operator refers to the similarity (cosine similarity) between two vectors. If the sample is classified as an unseen class ý E y Vunseen: this class will become a seen class Vseen + ÿ. and the model will treat this sample as the first instance of this seen class. The feature vector DAA(r(z)) will be used as the initial prototype for this class, denoted as Pg. Subsequent samples will be compared to all known and seen prototypes, including this new Pg-
For the test-time training, we update the DAA to involve knowledge from novel classes. For known classes, we continue to use the MSE loss, which penalizes deviations between the original and adapted features to ensure that the DAA does not alter the feature representations significantly. Converse to known classes, unknown classes are handled with a contrastive loss that encourages the DAA to produce features distinct from both known classes and other unknown classes. Specifically, given test batch data B, the test-time training with DAA can be represented by two kinds of loss functions.
5

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Known classes
Unknown classes
Sample Queue
Sumple
Queue
Randomly select
Renewal samples
f(x))
Replay
Feaure
samples
Queue
Predict
Unknown classes: Deer
Dog
(a) for known classes
(b) for unknown classes
Figure 3: STMR strategy. Replay samples are randomly drawn from the known wwwns memory to mitigate catastrophic forgetting in DAA, while renewal samples are selected from the unknown-class memory to refresh prototype representations and support timely memory updates.
The first kind of loss is the self-supervised learning loss via the pseudo labels, which is similar to the test-time training TTA:
(B, c)
EgEB (xn( =), c), Lun (B, ErEB (xn(x) c).
The second kind represents the replay for known and the renewal for seen classes via retrain samples in memory:
crplay km
(c) (2,, (x)(
Cremewal (c) = ((e,)))(( Lan(x).
(8)
un
The overall testing phase loss for the DAA is shown below:
Lust (B3) = IEEE.. (Cin(Bs Crppay (c) A2 ECEDiO (Cun(B, + Cocnewal (
(9)
where and A2 hyperparameters that balance the contributions of each loss term.
Training-free TTD vs. DAA. Training-fiee free TTD approaches such as HM 22 rely only a fixed model and static feature distribution, updating only class prototypes to accommodate novel classes. While effective in certain scenarios, such methods struggle with complex or overlapping feature distributions. In particular, when unlearned features from different unknown classes entangled, prototypes are constrained to represent mixed semantics, resulting in suboptimal dis- crimination and degraded classification performance. Moreover, the absence of model adaptation during test time limits their capacity to incorporate new knowledge. In contrast, our DAA enables dynamic model updates during testing, allowing the feature distribution evolve with incoming data. This adaptability improves the model's ability to disentangle and separate unknown classes while preserving representations known ones. By explicitly amplifying inter-class discrepancies and refining the feature space through continual adaptation, DAA achieves more robust generalization and higher accuracy open-world scenarios.
Discussion:
Experiment
4.1 Experimental Details
Dataset details. We conduct experiments based three benchmark datasets, namely CIFAR100 (C100) Caltech-UCSD Birds-200-2011 (CUB) 35 and Tiny ImageNet All these datasets split into known and unknown classes (7:3). The model is trained the known training set, and tested on the mixture of known and unknown test sets. We follow three transformed datasets used for discovery in TTD work: CIFARI00D. CUB-200D, and Tiny-ImageNetD. The dataset partitioning follows the scheme outlined in Table More details of the dataset construction can be seen in Appendix.
Table 1: Statistic of the used datasets.
CIFA R100D
CUB-200D
Iingene)))
Dataset
Labeled Known
Unknown No. of samples Known Unknown No. of samples Known
Unknown No. of samples
TrainSet TestSet
70
0
35000
140
0
4195
140
0
70000
60

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Table 2: Major comparisons on CIFARIOOD, CUB-2000 and Tiny- ImageNetD (Tiny-IND in table).
Real-time evaluation reflects the accumulated performance across all test batches, while post evalua- tion reassesses all test samples after training the DAA, updating the memory and prototypes. (Bold data is the best performance and Underline data is the second-hest performance.)
Real-time Evaluation
Evaluation
Method
KA1
HCA1
ARI:
NMI
VMI
KA1
HCAT
ARI:
NMI
VM
KF]
Threshold
76.4640.98
98
0.6440.01
0.42+0.01
0.8540.00
0.85+0.00
76.6241.85
0.6040.01
0.42+0.01
0.72+0.01
0.72=0.01
6.45+1.78
CIFAR
1.2P [X]
59.9242.15 15
0.53+0.00
0.3740.01
0.76+0.01
0.76+0.01
50.53+7.25
0.50+0.02
0.37+0.01
0.09+0.05
0.69+0.05
27.85+7.21
DP
87
66.09=1.01
0,58 00
0.44+0.01
0.8060.00
0.80=0.00
56,19:2.00
0.55+0.01
0.4420.01
0.72:0.00
0.72:0.00
29.0641.99
72.77a1.20
0.59
0.3
0.7840.00
0.78g0.00
67.21+2.53
0.5840.02
0.4640.01
0.7120.00
0.7120.00
17.69a2.53
PHE
44
68.1841:12
0.6010.01
0.4410.01
0.7360.00
0.7320.01
68.
68.2021107
07
0.58+0.01
0.42+0.00
0.70:0.01
0.70:0.01
2.15:0.92
HM
22
79.17=0.13
0.61
0.4340.01
0.8240.01
80.7341 0.82=0.01
59
0.6340.00
0.48+0.01
0.7340.00
0.7340.00
3.414149
Ours
80.81+0)44 0.66++0.01
0.52+0.01
0.85+0.00
0.85+0.00
80.27+40.0
0.65+0.01
0.53+0.01
0.75+0.01
0.78+0.01
3.10-+070
Threshold
66.09=1 20 0.53+0.02
0,20+0.01
0.83+0.01
0.83+0.01
65.52+3.68
0.48+0.00
0,20+0.00
0,70+0.00
0.30=0.00
1.61+3.55
CUB200D
L2P
46 36.22:1.53
0.49:00.0
0.28++0001
0,80+0.00
0.80=0.00
31,97+3.35
0.010001
0.27+0.01
0.71+0.01
0.71=0.01
42.2943.14
DP
17
53.69e1 24
0.55+0003
0.29+0.01
0.83+0.01
0.83+0.01
633 33.77733.27
0.48+0.02
0.24+0.01
0.48+0.00
0.48+0.00
5.85+3.11
GMP
3
62.97=1.33
0.57=0.03
0.29:00.0
0.84+0.00
0.84=0.00
58.11 3.00
0,48+0.01
0.26+0.01
0.71=00.00
0.71:0.00
5,4612.77
PHE
0.49:0.01
0,2840.00
0.82+0.01
0.82g0101
44.6340.95
0.49w0.01
0.24+0.00
0.65w0.01
0.65a0.01
3.96&1.11
HM
66.20+0055
0.52+0.01
0.34+0.01
0.83+0.00
0.83+0..00
64.42+0065
0.50+0.01
0.27+0.01
0.70+0.00
0.70+0.00
4.07+0447
Ours
68.09:0.33
0.6340.01
0.40:0.01
0.88:0.00
0.58=0.00
66.26:0.39
0.58+0.01
0.32:0.01
0.75:0.00
0.75:0.00
3.60:0.39
Threshold
57.53=11 80
0.57=0 00
0.31
0.85+0001
0.85=0.01
52.3643. 10 0.24:0.01
0.15+0.01
0.34:0.00
0.34:0.00
22,90+3 08
1,2P [8
46.25+1 41
0.51+0.01
0.33+0.01
0.81+0.00
0.81+0.000
29. 39.500+.77
0.43+0.01
0.33+0.01
0.69+0.00
0.69+0.00
47.97+3.77
Tog-IND
37
46.51:0.58
0.51
0.33+0.00
0.8000.00
0.81=0.00
28.5343.33
0.42+0011
0.32+0.01
0.68+0.01
0.68:0.01
47.57+3.32
GMP
62.47+1.40
0.5120.01
0.30
0.72x0.01
0.72+0.01
63.9542.04
0.5640.01
0.4340.01
0.7240.01
0.72x0.01
16.8642.04
PHE
44
58
13.39=1.2 29
0,55+0.02
0,25+0.00
0.86+0.01
0.86+0.01
58.39+1.T4
0.45+0.01
0.34+0.01
0.64=0.01
0.64=0.01
3.47+1.32
25.31=1.31
0.6]
0.3840.00
0.87+0.00
0.87=0.00
74.9442.00
0.5640.02
0,40+0.00
0.72+0.00
0.72+00.0
1.1
Ours
76_38+0.82
0.63+0.01
0.41+0.01
0.88+0.00
0.58+0.00
75.50+1.96
0.58+0.01
0.42+0.01
0.73+0.00
0.73-0.00
2.39+1.56
Implementation details. In our implementation, we build our method on the prompt-based method 1.P (38) which employs a ViT-B/16 backbone [13] following the pretraining procedure of NCD and GMP work. We employed the contrastive loss of the GCD literature when we fine-tune the pretrained model on the known classes, using SGD optimizer and cosine decay learning rate scheduler with an initial learning rate of 0.1 and minimum learning rate of 0,0001, and weight decay of 0.00005, All input images are resized to 224 x 224 and augmented to match the pretrained backbone settings.
Evaluation metries. Following metrics that traditional NCD methods use, including Hungarian Cluster Accuracy (HCA) [24]. Adjusted Rand Index (ARI) 28 Normalized Mutual Information (NMI) 23 and V-Measure [2]
22 we use the following metrics. We first provide some cluster
(1) measures the clustering accuracy by optimal one-to-one mapping between predicted clusters and
true labels using the Hungarian algorithm. (2) ARI quantifies the similarity between the predicted clustering assignments and the true labels while adjusting for chance. (3) NMI assesses the mutual dependence between predicted and true labels by shared information between the two distributions.
(4) VM simultancously constrains the purity and coverage of the clusters through the harmonic mean. For known classes, we also employ two key metrics to comprehensively assess the model's performance: Known Accuracy (KA) and Known Forgetting (KF). KA measures the traditional classification accuracy of the model on known classes while KF quantifies the degree of performance degradation on known classes over time.
For unknown classes, we use two agreement metrics: (1) True-label Agreement ratio (TA). This metric measures the maximum proportion of samples from given true class that are predicted as the same class; and (2) Cluster Agreement ratio (CA). This metric measures the maximum proportion of samples from given predicted cluster that have the same true label.
TA EPEyOT
max DE)
Σ EEE
1[0(z) = pl)
(10)
DDiest
CA- EpeN'sn Chest
max eE CE
(e 1(v c))
(11)
where 1(-) is the indicator function (1 if true, 0 otherwise). For more details of the metrics, see Appendix.
4.2 Major Comparisons
In this paper. we first compare our methods with naive thresholding- based training- free methods. When exceeding the threshold, the naive method will be considered to have discovered a new class. And we compare with some training-required methods including L2P (38], DP [37], and GMP 3 these methods update prompts like TTA and CL methods. We also compare with the recent PHE 44
method for OCD and HM [22) method for TTD.
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112.
4,25
5633
0
0 0m
am
CIFAR ReaHinne Eval
IMC Eval
CUB Real-time Eval
CBB Eval
(6) Tiny Real Same Eval
Tiy Post
Figure 4: Major comparisons (TA1, CAf) CIFAR CUB-2001 and Tmy-ImageNetDD. Real- time evaluation reflects the accumulated performance across all test batches, while post-evaluation reassesses all test samples after the whole test phase. Method A, B. C. D. E and F represent "Threshold", "L2P", "DP", "GMP", "PHE" and "HM".
Comparisons on clustering metrics. As shown in Table 2 across multiple datasets, our method demonstrates significant advantages in both real-time and post evaluations. In real-time evaluation, our method achieves the best performance on HCA, ARI, NMI, and VM metrics (e.g.. 0.66, 0.52, 0.85, and 0.85 on CIFAR; 0.63, 0.40, 0.88, and 0.88 on CUB; 0.63, 0.41, 0.88, and 0.88 on Tiny-ImageNet), This indicates that our method excels in hierarchical clustering, clustering quality, and matching accuracy. In post evaluation, our method also maintains the highest performance on these metrics (e.g., 0.65, 0.53, 0.75, and 0.75 on CIFAR; 0.58, 0.42, 0.75, and 0.75 on CUB; 0.58, 0.42, 0.73, and 0.73 on Sin--emageNee). further validating the superiority of our method in clustering and matching tasks. Additionally, regarding the knowledge forgetting (KF) metric, although knowledge updates may sometimes result in slightly higher KF values compared to methods that do not update the model, our experiments show that the forgetting of existing knowledge during the DAA update process is minimal. There is no forgetting, and our method exhibits good knowledge retention capabilities.
Comparisons on TA& & CA. In our analysis, TA and CA are employed to quantify the agreement within true labels and clusters, respectively. However, simply predicting most samples into a single new cluster can inflate either TA or CA values. This scenario, while focusing single metric, may not reflect meaningful clustering performance. Therefore, our goal achieve a balanced and maximized performance in both TA and CA. This ensures that the clustering results are not only consistent with the true labels but also maintain meaningful and distinct cluster structures. The analysis of our experimental results, as detailed in Fig. 4 reveals several key insights into the performance of various methods in the context of Test-Time Discovery (TTD). The comparison between training- -ased and training- free methods is particularly illuminating. Training-based methods, which update the model parameters upon encountering new classes, tend degrade in performance. This is attributed to the immediate adaptation to new classes, which often results in lower TA and CA metrics, along with an increased risk of catastrophic forgetting, where the model loses its ability to recognize previously learned classes. Conversely, training-free methods like HM, which do not update the model parameters, struggle to learn from new classes effectively. This limitation arises because the model's capacity to refine its representations in response novel classes is constrained. Our proposed method balances these extremes and yields more balanced performance across all three datasets, indicating that our method enhances the network's 's adaptability in discovering novel classes.
4.3 Analysis on DAA
Distance between prototypes. Fig. [ the prototype distance analysis highlights the superior effectiveness of the DAA method over HM baselines. Specifically, features learned with DAA exhibit larger prototype distances, indicating enhanced class separability, particularly for novel classes. We also compute the ratio of average intra-class to inter-class distances. A higher Intra/Inter ratio reflects tighter within-class clustering and greater between-class separation. As shown, the DAA method yields a significantly higher Intra/Inter ratio than HM methods, demonstrating its strong ability to produce more discriminative and well-structured feature representations for new classes.
Ablation study. Table 3 summarizes the ablation results. Activating TTT alone improves CA but leads to model instability and higher KF. Using only DAA (TA+CA: 76.00 real-time, 65.13 post) outperforms the fixed model (71.84 real-time, 64.34 post), confirming the effectiveness of discrepancy amplification. Combining TTT and DAA further boosts TA and CA in real-time settings, though KF remains high. Incorporating STMR into the full model yields the best overall performance (TA+CA: 78.42 real-time, 73.52 post) and reduces KF, demonstrating that STMR stabilizes prototype updates by mitigating interference from outdated representations and enhancing knowledge retention.
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MAK 1375
REs Time
310
-25
Sea vala 1.385
Avrng Nemeer
120
335
16
Figure 5: Distance between prototypes.
Figure 6: Different memory size.
Different Memory Size. Figure 6 shows the effect of memory size model performance. While larger memory can store more samples, may include outdated or irrelevant data, weakening prototype quality. Conversely, too small memory may fail to capture representative features, reducing generalization. Additionally, increasing memory size leads to higher computational cost. These results highlight the need to balance memory capacity and efficiency to ensure accurate prototype representation without excessive time overhead.
Different discoverable class numbers. Given that the distribution of known and unknown classes varies across different scenarios, Table g shows that crucial to appropriately set the number of discoverable classes to achieve optimal TTD performance. Our method demonstrates superior performance compared to the HM method across various settings. Specifically, when the number of discoverable classes is increased, our method generally achieves higher TA and CA, indicating better adaptation to new classes and more coherent clustering. However, if the number of discoverable classes far exceeds the true number, real-time evaluation shows improvements in TA and CA, but post-evaluation reveals a drop in TA and more severe forgetting indicated by higher KF. While a higher number of discoverable classes enhance adaptability, it may also introduce noise and disrupt the model's ability to retain knowledge. More experiment is mentioned in Appendix.
Table 3: Ablation study. T: Test-time Training, D:
Table 4: Comparisons of different dis- coverable class numbers.
DAA, S: STMR, T+C: TA+CA.
Real-time Eval
Post Eval
Known +
Real-time Eval
Post Eval
Unknown
CA
T+C
KF
TA
CA
T+C
CA
T+C
70+30
21.11
56.87
77.9%
31.03
34.81
65.84
3.47
23.30
48.54
71.84
24.53
39.80
64.33
2.04
90.10
80420
14.92
11:57
52.98
64.15
67.90
75.72
21.50
20:90
31.09
30.61
52.11
51.99
0.69
0.44
18.57
51.86
70.43
19.30
41.75
61.05
12.61
70+00
20.37
92.94
113.31
22.63
47.09
69.72
10.69
25.63
50.37
76.00
25.11
40.02
65.13
2.70
30.79
42.61
73.40
36.37
33.11
69.48
4.65
Ous
80+20
36.21
48.13
84.34
32.56
31,64
64.00
1.73
32.40
46.02
78.42
35.97
37.55
73.52
3.54
90+10
70100
31,78
11.81
89.58
30.55
121.36
72.39
22.35
31.00
27.96
49.32
71.67
58.96
11.07
1.57
Frenquency of STMR. The frequency of STMR plays crucial role in balancing the trade-off between model performance and computational efficiency. As shown in Fig. A lower frequency of STMR leads to insufficient updates and refinements of the model during the test phase, resulting in poorer recognition performance for both known and novel classes. While increasing the frequency of STMR can improve the model's ability adapt new data, also leads to substantial increase in computational overhead. Frequent STMR operations require more time and resources for processing each batch.
312
Figure 7: Trends of TA, CA and KF with different Figure 8: Trends of TA, CA and KF with different
STMR frenquency.
replay samples.
Different replay samples. The number of replay samples used in the memory replay mechanism affects the performance of our method. Fig. 8 shows that when no replay samples are used, the model exhibits the highest level of knowledge forgetting, as indicated by the largest KF value. As the number of replay samples increases, the KF value decreases at the cost of increased computational time. A well-tuned number of replay samples ensures that the model can effectively leverage memory replay to mitigate catastrophic forgetting while maintaining reasonable processing times.

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Post visualization using t-SNE. In Fig. 9 we employ t-SNE [33 to visualize the true-label distribu- tion of test samples. L2p and GMP methods that directly update the base model parameters often risk disrupting the model's existing structure, leading to destructive confusion between classes. HM methods, due their inability to update the model, maintain a static feature distribution, In contrast, our method strikes balance by actively disrupts the feature distribution and attempts to learn updates that separate features from different classes and keep a clearer boundary than other methods. Effect of DAA in our method can be seen in Appendix.
Some known classes maintaia
Rely on fixed feature
Confused
Confused
Unknowa Classes
Confused
Known Classes
(a) 1.2P
(b) GMP
(c) HM
(d) Ours
Figure 9: T-SNE visualization on CIFARIOOD with known and 5 unknown classes.
Comparison of unknown class label matching. Fig. 10 shows the matching between predictions and ground truth, where the base-model updating method easily makes chaos and fails to classify. In the Prototype-only method like HM, lots of old samples are classified into unknown clusters, while DAA w/o STMR performs better. DAA w/ STMR further corrects many classification relationships, increases the number of samples in unknown clusters and reduces the number of samples in old categories that are misclassified.
Classify
durdf
sample
classily
(a) GMP
(b) HM
(c) DAA without SR
(d) DAA with SR
Figure 10: Matching comparison between unknown class predictions and ground truth across methods on CIFAR-100D (90+10). Black rectangles indicate old class samples misclassified as unknowns.
5 Conclusion
In this paper, we introduced the Discrepancy-Ampliyy Adapter (DAA) and the Short-Term Memory Renewal (STMR) strategy tackle the challenge of Test-Time Discovery (TTD). By combining trainable adaptation with efficient memory management, our method enables accurate and dynamic identification of novel classes while preserving performance on known classes. DAA enhances feature discrimination by amplifying discrepancies between known and unknown categories, while STMR ensures adaptive and efficient prototype updates through short-term, sample-driven memory refresh. Extensive experiments across multiple benchmarks confirm the superiority of our approach in both real-time and post-hoc evaluations, consistently outperforming existing methods. Our results underscore the importance of real-time adaptability and memory design in TTD. While our method shows strong performance, it introduces additional computation during the warm-up and adaptation phases, and the heuristic-basee memory update may limit performance in highly noisy environments. In our future work, we plan to explore more lightweight and adaptive adapter architectures, as well as principled memory selection and compression strategies. Additionally, extending our framework to handle multi-modal or continual learning scenarios could further broaden its applicability in open-world environments.
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Acknowledgements
Our work was supported by the following institutions and projects: National Science and Technol- ogy Major Project (No. 2022ZD01 I 17901), National Natural Science Foundation of China (NOs. 62476189, 62406323), China Postdoctoral Science Foundation (No. 2024M753496) and The Post- doctoral Fellowship Program of CPSF (No. GZC20232993), Suzhou's key core technology "list hanging marshal" project (No. SYG2024149), Jiangsu Province Graduate Research and Practical Innovation Plan (No. KYCX25_3572).
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DAA: Amplifying Unknown Discrepancy for Test- Time Discovery
( (Appendix) )
Comparison of TTD task and other Category Discovery task
We compare TTD with related settings. Out-of-Distriution (OOD) 40 19 detection neither discovers novel classes adapts during inference. TTA (1] handles distribution shifts via self- supervised learning but assumes all test samples belong to known classes. NCD [[]] and GCD [4] both aim to identify unknown classes test time under offline inference paradigm through clustering the entire test set. OCD [8] resembles TTD in identifying known and unknown classes, but does not incorporate test-time learning from novel classes.
Test-Time Discovery (TTD) a challenging task that focuses on class shifts rather than domain shifts during test time. It requires the model to not only discover new classes but also classify them accurately while maintaining robust performance on previously seen classes. This dual requirement is particularly demanding due to several intrinsic complexities: the intricate nature of class discovery, the scarcity of labeled data for new classes, and the often ambiguous boundaries between classes. The key challenges in TTD can be summarized follows:
(1) Distinguishing between the discovery of new classes and the identification of already discovered ones.
(2) Learning and adapting to new classes with limited sample sizes during testing phase. Avoiding catastrophic forgetting, where the process of learning new classes can inadvertently degrade the model's performance on previously learned classes.
Table 5: Comparison between different category discovery settings.
Type
Train
Test
Discovery
Test-time Learning
OOD
Known classes
Shift Known classes
N/A
N/A
TTA
Known classes
Shift Known classes
N/A
Shift Known classes
NCD
Known classes
Unknown classes
Post
N/A
GCD
Known classes
Known classes + Unknown classes
Post
N/A
OCD
Known classes
Known classes + Unknown classes
Real-time
N/A
TTD
Known classes
Known classes + Unknown classes
Real-time
Unknown classes
B Details of datasets
We conduct our experiments using three widely recognized benchmark datasets: CIFARI00 (C100) [4), Caltech-UCSD Birds-200-2011 (CUB) [35]. and Tiny ImageNet [15]. Each these datasets is systematically partitioned into known and unknown classes. The model undergoes training on the known training set and is subsequently evaluated on a mixed set containing both known and unknown classes. Since the primary objective of these datasets is facilitate new class dis- covery, we follow the HM (22) and use the transformed versions as CIFAR10OD. CUB-200D, and Tiny-ImageNetDD reflect this adaptation.
The dataset partitioning follows the scheme outlined in Table 6 Specifically, during the training phase, divide the training set into known and unknown classes based on their class index order. For instance, in CIFAR 100, the first 70 classes are designated as known, while the remaining 30 classes treated unknown. The supervised training process is then conducted using only the known classes within the labeled training set. More precisely, CIFAR10OD consists of classes 0-69 (70 known classes in total), CUB-2000 includes classes 0- 139 (140 known classes in total), and Tiny-ImageNetl comprises classes 0-139 (140 known classes in total), all of which are utilized for training.
During the test phase, the model evaluated on the entire unlabeled test set, which includes samples from all categories, enabling new class discovery and classification. While the category labels remain
21

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structured according to the original for CUB-200D and Tiny-Iman Imy-eeeeeNet)) these labels are only used for metric evaluation and are not provided to the model during inference. This setup ensures a realistic scenario for open-won world learning, where the model must autonomously identify and categorize previously unseen classes.
splits (e.g., 70+30 for CIFARIOOD and 140+60
Table 6: Statistic of the used datasets.
CIFAR10OD
CUB-2001
Tiny- ImagenetD
Dataset
Labeled|
Known
Unknown No. of samples Known
Unknown No. of samples Known
Unknown No. of samples
TrainSet TestSet
70
0
35000
140
0
4195
140
0
70000
30
10000
60
5794
140
60
10000
C
Metric Definition
The evaluation process is structured into two distinct parts: one focusing on known classes and the other on unknown classes. To ensure comprehensive assessment, follow HM [22] and employ both real-time evaluation and post-evaluation strategies. For test-time evaluation, real-time performance is a critical factor. Thus, we compute and report real-time scores for all evaluation metrics as the model processes each test sample. This approach provides immediate insights into the model's performance and enables dynamic tracking of classification accuracy and discovery efficiency, Alongside these real-time scores, we present the final accumulated values, which represent the overall average performance across the entire test set. In addition, recognizing that traditional novel class discovery (NCD) methods typically rely on post-evaluation, we also incorporate this approach for comparative analysis. post-evaluation, all test samples are revalidated collectively after the entire test phase is complete. This post-hoc evaluation allows for more refined assessment by leveraging the full distribution test samples, potentially improving class assignment and clustering accuracy. By providing both real-time and post-evaluation scores, we ensure a thorough and balanced evaluation of the model's effectiveness handling both known and unknown classes.
C.1 Metrics for known classes
For the evaluation of known classes, we employ two key metrics comprehensively assess the model's performance: Known Accuracy (KA) and Known Forgetting (KF).
(1) Known Accuracy (KA). KA measures the traditional classification accuracy of the model on known classes, reflecting its ability to correctly recognize and classify samples that were part of the training set. This metric serves as a standard benchmark for evaluating the retention of previously learned knowledge:
KA D
Σ 1(0(x) =) ZED
(12)
Diest
where Vknown is set of predefined known classes, Dis is test samples with ground -oound-truth class c, ((x) is the predicted label for sample v, 1-) is the indicator function (1 if prediction matches true class otherwise)
(2) Known Forgetting (KF). KF, on the other hand, quantifies the degree of performance degradation on known classes over time. It captures the extent to which the model forgets previously learned information as encounters new data, particularly when adapting to novel classes. A lower KF score indicates better knowledge retention, while higher score suggests significant forgetting.
KF KApre KApost
(13)
where KApost and KApcc are the KA computed on all test data with known classes, before and after Testing Phase.
C.2 Metrics for unknown classes
For unknown classes, since the predicted label space yTT does not match the cluster label space Deecm propose agreement metrics to assess effectiveness. In the test set Diest sample x has
22

---

true label y E Veen and a predicted cluster label ((x) E Yeen We define the subset of Dies with true seen label as Diss and the cluster with predicted label p as Chea
(1) True-label Agreement ratio (TA). This metric measures the maximum proportion of samples from given true class that are predicted as the same class:
(E 1[5(z) = p)).
TA EE EETT yXT
max
(14)
DDistt
pE
where 1(:) is the indicator function (1 if true, O otherwise).
(2) True-label Entropy (TE). This metric measures the average entropy H()) of the predicted labels for samples with that true class:
TE = Eeeyer vom H(((())) DE
(15)
where H() is Shannon entropy of predicted label distribution which is used to quantifies uncertainty or diversity in a distribution, H (24) = Exez g(z) log2 ((2).
(3) Cluster Agreement ratio (CA). This metric measures the maximum proportion of samples from given predicted cluster that are with the same true label:
1(y 1(0= 1(==))
CA Epeyan
(16)
Chest
CE CE
(4) Cluster Entropy (CE). This metric measures the average entropy of the samples that predicted the true class contained in clusters:
CE = EpEy.n H(Iv(( E Cp }).
(17)
C.3 Clustering metries
Traditional novel class discovery (NCD) methods typically rely post-cluster evaluation, where the quality of the discovered clusters is assessed after the entire test set has been processed. To ensure comprehensive comparison with existing approaches,
also report several widely used
clustering evaluation metrics, including Hungarian Cluster Accuracy (HCA) (4), Adjusted Rand
Index (ARI) (28). Normalized Mutual Information (NMI) 23 and V-Measure 29 Note that these
metrics are only evaluated after TTD, say post evaluation.
(1) Hungarian Cluster Accuracy (HCA). This metric measures the clustering accuracy by computing an optimal one-to-one mapping between predicted clusters and ground-truth labels using the Hungar- ian algorithm. It provides an intuitive evaluation of how well the discovered clusters align with the actual class distributions. HCA can be computed as
HCA = E(z.)) (y = m=p(((=))),
(18)
where map(-)is the optimal mapping from clustering to labels obtained based the Hungarian algorithm
(2) Adjusted Rand Index (ARI). ARI quantifies the similarity between the predicted clustering assignments and the ground-truth labels while adjusting for chance. It accounts for both correct pairwise clustering and misclustered pairs, offering a robust measure of clustering consistency.
RI ER
ARI
(19)
max RRI) E
where Rand Index (RI)
a+ is the logarithm of samples of the same class assigned to the same C2
cluster, and b the logarithm of samples of different classes assigned to different clusters. n the total number of samples, combination C² mfn-1) and E[Ri] is the expected value of RI.
2
(3) Normalized Mutual Information (NMI). NMI assesses the mutual dependence between predicted and true labels by measuring the shared information between the two distributions. A higher NMI value indicates better alignment between the discovered clusters and the actual categories. The value
23

---

interval of NMI is [0.1]. the clustering results and the real labels.
J. and larger value indicates higher degree of information sharing between
2. 2.T(U:V)
V)I H(U) H(V)
(20)
where U collection of true labels and is collection of predictions. T(24; V) is Mutual In- formation where I(1;; V) H(U) H())). H(U) is the entropy of true label, (
P p(c) p(c), and H(V) is the entropy prediction, HIV) - E
p(k) l(k).
(4) V-Measure (VM). The VM is taken in the interval [0,1], which simultaneously constrains the purity and coverage of the clusters through the harmonic mean. Both VM and NMI symmetric metrics that support the comparison of clusters and categories at different scales.
2-h
V-Measure = h
(21)
where homogeneity h 1 H(M)
H()) and completeness =
HOVI HQ)
H
Ei=n Et= t),, t) log p(k,0)
((k) and (V(2) Ei= Ex= p(t, log p(t.k)
where p(t) N₂
n(3)
is the sample proportion of class 1, p(k) No is the sample proportion of cluster ki and p(t, k) = N
N
countx(() is the joint distribution probability.
D More TTD Comparisons
In this section, we provide detailed comprehensive comparison of our method with several ap- proaches on three benchmark datasets: CIFAR10OD, CUB-200D, and Tiny-ImageNetD. The evalua- tion includes both real-time and post evaluations, where real-time evaluation reflects the accumulated performance across all test batches, and post evaluation reassesses all test samples after training the DAA, updating the memory, and prototypes. The results are summarized in Table 7
D.1 Comparisons on TA & CA
TA and CA are critical metrics for evaluating the performance of test-time discovery methods. TA measures the overall accuracy of the model in predicting the correct class labels, while CA evaluates the model's ability to correctly classify samples within each class. A balanced performance both TA and CA indicates that the model not only achieves high overall accuracy but also maintains meaningful and distinct class structures. Analysis is given in the main text.
D.2 Comparisons on TE & CE
TE and CE are complementary metrics to TA and CA, respectively. Lower values of TE and CE indicate better performance. However, Our method not training-free, which means it updates the model's representations during test time. This adaptive updating mechanism allows our method to refine its predictions and improve performance. As result, our method inevitably has higher TE and CE values compared to training-free methods like HM.
And the results demonstrate that our method achieves a balanced and maximized performance in both TA and CA, while also maintaining relatively low TE and CE values. This is attributed to our DAA and STMR mechanism, which allows the model to refine representations in response to new classes without suffering from catastrophic forgetting. Unlike training-fret free methods that struggle learn from new classes, our method leverages the benefits of continuous adaptation improve performance. Compared to other model-training methods, our method shows superior robustness and adaptability, making a more effective solution for test-time discovery tasks.
E Different discoverable class numbers
In our experiments, we set an upper limit the number of discoverable classes to investigate its impact on the performance of test-time discovery (TTD). This is a crucial parameter, as real-world scenarios may involve a much larger or even infinite number of potential new classes. The results

---

Table 7: More TTD comparisons on CIFARI0OD, CUB-200D, and Iny-ImageNetD (Tiny-IND IND in table). Real-time evaluation reflects the accumulated performance across all test batches, while post evaluation reassesses all test samples after training the DAA, updating the memory and prototypes. (Bold data is the best performance and Underline data is the second-best performance.)
Method
Real-time Evaluation
Post Evaluation
KA1
TAT
TR.
CAT
CRI
KA-
TAT
TEL
CAT
CRI
KF]
Threshold
76.1610.98
17.21a1.33
0.52+0.01
36.9113.26
2.0740.41
86.6241.85
34.60a2.02
1.101004
18.70u1 24
1.420.05
6.45.1.78
CIFARI00D AARR00D
L,2 338
59.9312.15 15
8.5712.49
0.6020.06
43. 13.1033330
1.8510 18
50.5347.25
9.6021.50
0.7710.12
27. 27.3912.11 11
1.3700.21
27.8527.21
DP 17
66.09=1001
8.80=1.69
0.53
48 545666778
163400330 30
56.1942.00
8.68=2.06
0.70
28. 38.9342.22
25
1.340.05
29.06s1.99
GMP 13
72.77a1.20
7.3740.88
0.51
12.2641.54
1.
67.2142.53
13,0142.59
1.1810.06
27.10
1.5540.08
17.6942.53
PHE
68.18+1.12
0.1100003
1.9640
68.20+1.07
12.17=1.01
1.0+0.03
21.56+1
1.6800.06
2.15=0.92
HM
79.17:0.13
2113+4062
0.67+0.02
56.3721142
1.2340.08
80.7341.55
51,0321.24
10740.02
34.814122
1.5020.02
3.41=1.49
Ours
80.81-0.44
31.94+0.98
0.76+0.01
45.
48-0888
1.5340.06
80.27+0.60
35.63=1.11
15400.06
37.31+1.30
1.55+0.09
4.10=1660
Threshold
66.0911.20
43.6012.08
0,4010.06
44.0514.96
1.1610.00
65.5243.68
19.90a1.33
0.8110.00
6.6/18.67
0.70.0.00
1.613..55
CL3200
L2P [38
46.22:1.555 9.07
0,44:0.02
55.7777779
0,9710 25
31,9743,35
4.75
0.51
24.4811
65
0.62:0.06
42.29:3.14
DP 13
13.69:1.24 43.68:22.20
0.4020.06
45.6815.88
1.50 36
63.3743.27
45.91
16990..0
7.92x1 1110
10
5.8523.11
GMP 13
62.9711.33
46.M11.87
0.59g0.03
17.9911.31
1.1910.13 13
58.1113.00
18.0241.20
L.5310 01
10.311145
0
0,9010.00
5.1622.77
PHE
44.66e1.03
28.64+1.43
0.63=0.02
59.17+2.88
0.93+0.03
44,6240.95
13.49:0.43
1.28+0044
18.73+1
1,5000.
3.96=1.11
HM
66.2010.55
58.3012.37
0.3540.02
13.3316.10
1.9220.83
64.4240.65
65.28
1.02+0.03
37.25541.00
1.24
4.07
Ours
68.09=0.33
$4.49+1003
0.39=0001
58 14+1.35
05
66.26+10.39
64.1222,23
12
44.65+0 88
1,29=0.13 13
3.60=10339
Threshold
57.53u1.80
11.35a1.55
0.48:0.03
63 53.3555555
0.6611.11
52.3613.10
6.48
0.37 02
13.8112.11
0,44:10:2
22. 22.9013.06
Tiay-IND IND
DP 37
L2P 58
46.5120.58
46.25=1.41
6.41100.3
7.79+2.92
0.51:0003
0.5110003
53 57
28.2766.10
1.15.0.21
1.33+00 23
28.5343.33
79,
10.38:2.42
9.6312.10
0.8510.02
09
26.
2810.79
38
1.37::0.06
1.3310.02
47.55733.32
47.97:3.77
GMP 13
62.4711.40
6.2511.72
0.45:0.02
38.0214.22 29
1,0810 14
63.9512.04
15.6122.63
3010
36.3112333
1.5420.03
16.86.2.04
PHE
58.39
13.94a0.90
0.48+0.02
71
0.40:0.03
58.39=1.44
10.
0.70+0.02
64464414
1.24:0.01
3.47
HM
25.3
16.0110.76
0.51
73.112..7
0.6]40.01
71,9142.20
16,23-1.21
0.8110.00
37.4311.30
1.2110.02
1.1522.18
Ours
86.38+0.2
24.10+0.50
0,54=0.00
71. 21.0022002
0. 0.73+0.05
75.50=199
23.17=1.35
1.140.01
36.41+1112
1.4400.01
2.39=1.56
summarized in Table 8 where we compare the performance of our method with the HM method under different settings. We find that increasing the number of discoverable classes generally improves TA and CA. but the effect depends on the number of known classes. For example, when the number of known classes is fixed at 70, increasing the number of discoverable unknown classes from 30 to 100 and then to 200, both TA and CA improve significantly. Specifically, for our method, TA increases from 32.40 to 36.21 and then to 41.41 1884 while CA increases from 46.02 to 48.13 and then to 30.55 (note that CA drops slightly when the number of unknown classes becomes very large). Increasing the number of discoverable classes generally leads to higher KF values, indicating more severe forgetting. For example, when the number of discoverable classes increases from 30 to 200, the KF value for our method increases from 3.54 to 8.27.
Compared to the HM method, our method shows more balanced performance across different settings. For instance, when the number of discoverable classes is set to 100, our method achieves a TA of 36.21 and a CA of 48.13, which are significantly more balanced than those of HM (TA: 17.58, CA: 81.28). This indicates that our method is more effective in balancing the discovery of new classes and the recognition of known classes. However, when the number of discoverable classes far exceeds the true number, both methods suffer from performance degradation. For example, in the case of 70 known classes and an infinite number of discoverable unknown classes, our method achieves TA of 31.78 and a CA of 89.58, while HM achieves a TA of 20.37 and a CA of 92.94. This suggests that while our method is more robust in general, both methods struggle when the number of discoverable classes becomes excessively large.
The results highlight the importance of appropriately setting the number of discoverable classes for optimal TTD performance. While more discoverable classes generally improve the model's s ability to recognize new patterns, they also introduce more complexity and risk of forgetting. Our method shows a more balanced performance across different settings, achieving higher TA and CA values while maintaining reasonable TE, CE, and KF values. This demonstrates the effectiveness of our approach in balancing adaptability and stability during test-time discovery.
F Architecture of DAA
We insert the lightweight DAA after the frozen ViT backbone to project the 768-d feature into an updated latent space. The architecture of DAA is a standard 2-layer adapter [2] which includes Linear down-projection layer 768 - 128 ReLU Activation and a Linear up-projection layer 128
+ 768. Our technical contribution is the training strategy that endows this simple structure to
25

---

Table 8: Comparisons of different discoverable class numbers.
Known
Real-time Eval
Post Eval
Unknown
TA
TE
CA
CE
TA
TE
CA
CE
KF
70+30
21.11
0.66
56.87
1.27
31.03
1.07
34.81
1.50
3.47
70+100
17.58
0.70
81.28
0.44
25.03
1.82
40.74
1.05
6.46
HM
7(+-200
19.86
0.76
85.60)
0.33
26.87
2.17
42.63
0.84
7.79
70+0c
20.37
0.84
92.94
0.16
22.63
2.86
47.09
0.46
10.69
70+Human
52.10
0.48
42,96
1.68
48.27
1.19
49.44
1.24
5.81
70+30
32.40
0.74
46.02
1.52
35.97
1,45
37.55
1.53
3.54
Ours
70+ 100
36.21
0.87
48.13
0.69
32.56
2.05
31.44
1.22
6.73
70+-000
41.84
0.90
30.55
0.67
31.00
2.22
27.96
0.93
8.27
70+0c
31.78
0.98
89.58
0.35
22.35
2.99
49.32
0.55
11.07
70+Human
75.22
0.35
30.70
1.97
49.30
1.35
50.61
1.36
2.68
maintain known class features and amplify discrepancy among unknown class features to gain better open-world test-time discovery behavior.
Unlike existing training-free free TTD methods like HM, our approach can make DAA have better ability distinguish between known classes and unknown classes during the warm up phase and trainable during the testing phase.
Compared traditional methods of updating the entire backbone, we only update DAA and will not damage the entire backbone.
G T-SNE visualization of the effect of DAA.
To provide more intuitive understanding how our method affects the feature space, we conducted T-SNE [33] visualizations of the feature embeddings before and after applying our DAA (Dynamic Adaptation and Augmentation) mechanism. The results are shown in Fig.
In the pre-training phase, our method attempts to disrupt the feature representations of unknown classes to some extent. This is evident from the visualization Fig. [Tbb] where the feature em- beddings of unknown classes are more scattered and less well-separated from known classes. This disruption is intentional, as it helps the model to avoid overfitting to the initial feature space and encourages it to adapt more flexibly during the test phase. After test-time training with DAA, the boundaries between known and unknown classes become clearer again, as shown in Fig. TTe This indicates that method effectively refines the feature space during test-time training, allowing the model to better distinguish between known and novel classes. In contrast, baseline methods that fix the model and do not change throughout the entire test phase (as shown in Fig, Ilab struggle adapt to new classes, resulting in less clear boundaries and poorer performance.
This visualization demonstrates the effectiveness of our DAA mechanism in dynamically adapting the feature space during test-time training, leading improved performance in recognizing both known and novel classes.
H Hyper-parameter analysis
H.1 Frenquency of STMR.
The frequency of STMR plays crucial role in balancing the trade-off between model performance and computational efficiency. As shown in Fig. 12, we conducted experiments to investigate the impact of varying the frequency of STMR the overall performance of our method. The results indicate that as the frequency of STMR decreases, both the TA and CA deteriorate significantly. This suggests that lower frequency of STMR leads to insufficient updates and refinements of the model during the test phase, resulting in poorer recognition performance for both known and novel classes.
On the other hand, while increasing the frequency of STMR can improve the model's ability to adapt to new data, also leads substantial increase in computational overhead. Frequent STMR operations require more time and resources for processing each batch, which can be impractical for
26

---

Unknown Classes
Known Classes
(a) Training-free method like HM
(b) DAA after warmuup
(c) DAA after post-cval
Figure 11: T-SNE visualization of the DAA's effect on CIFARI0OD with 5 known and unknown classes.
real-time or resource-constrained applications. Moreover, the improvement in performance brought by each additional STMR operation diminishes as the frequency increases, indicating that there is point of diminishing returns.
Therefore, selecting an optimal frequency for STMR is essential to achieve a balance between performance and efficiency. moderate frequency ensures that the model can effectively leverage STMR for continuous adaptation while avoiding excessive computational costs.
45.0
42.5
CA
Time
320
Agreement (%)
40.0
Time (sec)
300
37.5
35.0
280
32.5
240
27.5
220
10
never
2
10
Figure 12: Trends of TA, CA and KF with different STMR frenquency.
H.2 Different replay samples.
The number of replay samples used in the memory replay mechanism is another critical hyper- parameter that affects the performance of our method. Fig. 13 shows that the number of replay samples has significant influence the model's ability retain knowledge from previous classes while adapting to new ones. When no replay samples are used, the model exhibits the highest level of knowledge forgetting, indicated by the largest KF value. This suggests that without replay, the model is more prone to catastrophic forgetting, where quickly forgets previously learned information it adapts
data.
As the number of replay samples increases, the KF value decreases, indicating that the model is better able to retain knowledge from previous classes. However, this improvement comes at the cost of increased computational time, as more replay samples require additional processing during each batch. Moreover, while moderate number of replay samples can help stabilize the model's performance, an excessive number of replay samples can lead to diminishing returns in terms of performance gains, while further increasing the computational burden.
Thus, choosing an appropriate number of replay samples crucial for achieving balance between knowledge retention and computational efficiency. A well-tuned number of replay samples ensures that the model can effectively leverage memory replay to mitigate catastrophic forgetting while maintaining reasonable processing times.
27

---

45.0
320
42.5
TA
CA
Time
KF
310
40.0
Time (sec)
300
Aggrement
37.5
35.0
290
32.5
280
30.0
270
27.5
25.0
260
Figure 13: Trends of TA, CA and KF with different replay samples.
H.3 A and X2 during TTT
During test-time training (TTT), the overall testing phase loss for the DAA shown below:
Liea(B) A1 - (Aan(B,e) + creplay (c) + A2 BeeD (Cun c)
crenewal
(c)), (22)
where and A2 are hyperparameters that balance the contributions of each loss term.
We explored the impact different ratios between A and A2 on the overall performance of our method. The results presented in Table 9 The MSE loss is used refine the model's predictions for known classes, while the contrastive loss encourages the model to separate the feature embeddings of known and unknown classes. We conducted experiments with different ratios of MSE loss to contrastive loss to determine the optimal balance between these two objectives.
When the model places much higher emphasis on refining the predictions for known classes. This results in a relatively high TA and CA. However, model's ability to distinguish between known and unknown classes is somewhat limited, as indicated by the higher CE value. As the ratio decreases, the model starts to pay more attention to the contrastive loss, which helps improve the separation between known and unknown classes. This leads to slight increase in CA and decrease in CE, indicating better class separation. However, the overall TA and TE values are affected, suggesting that the model's ability accurately classify known classes is slightly compromised. When the ratio further decreases, the model crashes, as indicated by the extremely high TA and TE values and the near-zero CA and CE values. This suggests that excessive emphasis the contrastive loss destabilize the model, leading to poor performance.
These results highlight the importance of carefully balancing the MSE loss and contrastive loss during TTT. optimal ratio ensures that model can effectively refine its predictions for known classes while also maintaining clear boundaries between known and unknown classes. This balance is crucial for achieving high accuracy and robustness in recognizing both known and novel classes.
Table 9: Comparisons of ratio between l and Ag during TTT.
Real-time Eval
Post Eval
A1 : A
TA
TE
CA
CE
TA
TE
CA
CE
KF
1000:0.1
28.41
0.80
44.91
1.56
29.50
1.49
30.53
1.74
0.92
1000:0.5
29.40
0.82
45.80
1.55
33.33
1.53
34.66
1.73
1.23
1000:1
33.66
0.66
49.81
1.39
34.8
1.03
37.23
1.48
2.64
1000:2
32.40
0.74
46.02
1.52
35.97
1.45
37.55
1.53
3.54
(crasee))))
78.22
0.20
19.00
0.38
100
0.00
1.00
1.99
500:
30.20
0.84
42.64
1.64
33.30
1.48
34.37
1.71
3.76
500:
34.98
0.64
49.68
1.40
37.60
32.33
1.53
4.48
500 3(crahhd))
81.59
0.35
15.37
0.00
2.20
28

---

H.4 Comparison of different threshold Gamma
During testing phase, we base on the max similarity scores, and apply confidence threshold ry to determine whether a test sample belongs to seen class or an unseen class.
arg ca (PL (P(z)), max, P(x) > 7,
=
(23)
new unseen class,
otherwise,
where prediction comparison P(x) sim (DAA]][((r)))
Y selected through grid search in experiment, and analyzed the impact of The typical threshold of is 0.7. And our method is not sensitive to selection from one dataset to other datasets.
Table 10: Comparisons of different y during Testing phase.
Real-time Eval
Post Eval
TA
TE
CA
CE
TA
TE
CA
CE
KF
0.5
11.85
0.83
44.36
1.61
30.03
1.39
37.14
1.67
5.89
0.6
20.04
0.81
48.07
1.56
29.83
1.46
34.25
1.78
4.43
0.7
32.40
0.74
46.02
1.52
35.97
1.45
37.55
1.53
3.54
0.8
29.90
0.84
42.78
1.65
29.60
1.55
28.65
1.82
7.96
0.9
12.75
0.81
46.00
1.53
31.10
1.73
27.64
1.72
10.78
29

---

I
Overall algorithm
To improve clarity, we provided an overall algorithm follow.
Algorithm DAA Training and Test-Time Discovery with STMR
Require: train {( Ji with known classes )an i test stream Dist with unknown classes; warm-up frozen backbone f. trainable adapter warm-uuu epochs E, memory size M, confidence threshold
Ensure: Predictions y for test samples, dynamically updated 0 and prototypes.
Phase 1: Discrepancy-Ampliýf
Warm-up
initialize 0 randomly
2: for epoch 1,..., F do
3:
for mini-batch B C Draain
4:
S(B)
backbone features > synthetic unknowns preserve known
5:
f mixup(r) + (( Σ)
6:
Lun MSE (r), r)
7:
L.n + contrastive(AAA(()) (F))
> amplify discrepancy
8:
Luain Lin XCmn
9:
1+0-- VaLurano
10: end for
11:
end for
Phase 2: Test-Time Discovery with STMR
12: (Skn, Fin) +- moaa--kno--rrooyppess (Paiii))
13: Sun < -0; Fun < (); Veeen +- Vk
14: for each test batch B do
15:
r- r
(rA
16:
compute cosine similarity P(c) between and all prototypes (Po | c E Vsecn
< (cc)
18:
if' max P > then
assign Ø
known/seen class
20:
else
"new new_uunknwwn; Dwen + Vseen {ū} initialize new prototype Pa mean(z)
22:
23:
end
STMR memory renewal (every T batches)
if ú E Ym and batch match_idmodi
then
25:
for E Sun [0] do Trenew + (AA)(f(z)) if P(zeneew) ) Vin then
26:
27:
28:
discard x
> remove mis-classified known
29:
else
30:
Fualū) enqueuee (2renww
31:
end if
32:
end for
33:
end if
34:
FIFO update:
35:
Sun [+] enqueue (B); Fun enqueue(z)
36:
Liest compute-loss (2, U. Sun Fun)
Eq. (6)
37:
0+0 8 - 1) Vo
& self-supervised update
38:
end for
30


Reviews:
[{"review_id": "tKsBVp4xeL", "reviewer": "Reviewer_NwUb", "summary": "This paper proposes DAA, a trainable module for Test-Time Discovery that amplifies feature discrepancies to detect novel classes while preserving known-class performance. Experiments show improved novel class discovery and stable performance.", "questions": "see weaknesses and try to solve all of my concerns.", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. The problem addressed in this work is well-motivated.\n2. The paper is solidly organized, and the use of illustrative figures effectively helps readers grasp the core challenges and the proposed solution.\n3. Although the proposed DAA strategy is relatively simple, it effectively addresses limitations of existing methods and achieves notable performance improvements.\n\nWeaknesses:\n1. According to the strict definitions of test-time adaptation/training, accessing source datasets during inference is typically disallowed. If the test-time discovery (TTD) setting is an extension built on these assumptions for novel class discovery, then the reliance on source-known samples in DAA’s Short-Term Memory raises concerns. The authors appear to directly store known samples from the source dataset during test time. I suggest conducting an ablation study where the memory pool is constructed solely from test-time samples, to evaluate whether such a change significantly degrades performance.\n2. The implementation details of DAA are insufficiently described. Does it explicitly incorporate any mechanism designed to preserve or enhance feature-level discrepancy for unknown samples in the feature space?\n3. Eq.(2) is unclear:\n (a) How are the class labels for synthesized unknown samples defined?\n (b) Are the two loss terms in Eq.(2) weighted equally, or is there a balancing factor?\n4. The memory update mechanism in Short-Term Memory is not clearly explained. Is the sample filtering policy purely based on FIFO, or are there additional criteria for selecting reliable samples?", "was_revised": true, "final_justification": "As most of my concerns have been addressed, I will raise my score.", "rebuttal": "Thank you very much for raising these important review points.\n\n**Q1: If replay memory stores known samples from the source dataset during test time**\n\nWe agree that the strictest formulation of test-time training typically prohibits access to source data. However, prior works in both test-time adaptation and TTD often relax this constraint for the purpose of evaluating knowledge retention and stability. For example, methods such as CoTTA [1] and RoTTA [2] utilize source data during adaptation, and the previous HM method for TTD also maintains known-class samples throughout the test phase. Our usage of stored known samples in DAA’s Short-Term Memory (STMR) aligns with this precedent, aiming to mitigate forgetting of known classes.\n\nTo directly address the reviewer’s concern, we conducted an ablation study comparing different initialization strategies for the known-class memory, including settings that exclude training data entirely. The results are summarized below:\n\n| Initialize               | TA(real-time) | CA(real-time) | TA+CA(real-time) | TA(post)  | CA(post)  | TA+CA(post) | KF       |\n| ------------------------ | ------------- | ------------- | ---------------- | --------- | --------- | ----------- | -------- |\n| Empty (fixed after full) | 31.25         | 45.21         | 76.46            | 33.87     | 38.42     | 72.29       | 4.02     |\n| Empty (FIFO)             | 30.25         | 46.22         | 76.47            | 32.16     | 37.79     | 69.95       | 5.10     |\n| **Training set (fixed)** | **32.40**     | **46.02**     | **78.42**        | **35.97** | **37.55** | **73.52**   | **3.54** |\n\nWe observe that:\n- Even when the memory is initialized as empty and constructed entirely from test-time samples (with or without updating), our method still maintains competitive performance.\n- Using training-set-based initialization yields slightly better performance and lower forgetting, confirming its stabilizing effect on continual adaptation.\n- Importantly, the performance drop in the test-only setting is relatively modest, indicating that our method is robust even without access to training data at test time.\n\nThese results reinforce that while using source-known samples can further enhance performance, our method remains effective under stricter constraints.\n\n[1]Wang, Qin, et al. \"Continual test-time domain adaptation.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022.\n\n[2]Yuan, Longhui, Binhui Xie, and Shuang Li. \"Robust test-time adaptation in dynamic scenarios.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023.\n\n**Q2: Implementation details of DAA**\n\nThank you for pointing this out. DAA is explicitly designed to both preserve known-class representations and amplify feature-level discrepancies for unknown classes through tailored training strategies applied to a lightweight adapter layer.\n- For **preserving known-class features**, we apply a mean squared error (MSE) loss between the backbone features and the DAA-adapted features, ensuring minimal distortion for known representations.\n- For **amplifying inter-class discrepancy among unknown classes**, we employ a contrastive loss on the adapted features of simulated or discovered unknown samples, encouraging separation in the feature space.\n\nDuring test time, the same dual-loss strategy is applied immediately after prediction to enable continual adaptation. Moreover, the Short-Term Memory Renewal (STMR) mechanism enhances this process by selectively replaying known samples and renewing unknown prototypes, which further guides DAA’s online updates and maintains discriminative capacity throughout the test stream.\n\n**Q3: Eq.(2) is unclear**\n\nWe further modified the equation to avoid confusion and increase readability. The Eq.(2) was modified as follows:\n$$\n\\mathcal{L}_ {\\text{un}}(x) = -\\log \\frac{\\exp(\\sigma[\\text{DAA}(\\mathbf{\\tilde{r}}(x)),\\text{DAA}(\\mathbf{\\tilde{r}}(x))_ {aug}])}{\\sum_ {x' \\in \\mathcal{B}} \\exp(\\sigma[\\text{DAA}(\\mathbf{r}(x')), \\text{DAA}(\\mathbf{\\tilde{r}}(x'))])}\n$$\n\nHere, `x'` iterates over all samples in the mini-batch $\\mathcal{B}$ including known classes samples and simulated unknown samples. $\\text{DAA}(\\mathbf{\\tilde{r}}(x))_ {aug}$ is the feature obtained through data augmentation operations.\n\n**Q3(a): Class labels for synthesized unknown samples**\n\nDue to the contrastive loss **only needs to know whether they belong to the same category or different**. For example, $\\text{DAA}(\\mathbf{\\tilde{r}}(x))$ and $\\text{DAA}(\\mathbf{\\tilde{r}}(x))_{aug}$ are simulated samples and data augmented simulated samples, which are considered the same label. Different samples in the mini-batch $\\mathcal{B}$  are considered different.\n\n**Q3(b) Weight of two loss in Eq.(3)**\n\nWe will add the weight parameters to avoid assuming a 1:1 ratio, and we will include different comparison results as below in the appendix. Due to the significant difference in the magnitude of the two losses, if a 1:1 ratio is used, the model will collapse. The weight of two loss **during warm-up phase is fixed (200:1 in paper)**\n\n\n| MSE:Contrast | Real      | -        | time      | Eval     | Post      |          |           | Eval     |          |\n| ------------ | --------- | -------- | --------- | -------- | --------- | -------- | --------- | -------- | -------- |\n|              | TA        | TE       | CA        | CE       | TA        | TE       | CA        | CE       | KF       |\n| 1000:1       | 27.89     | 0.68     | 44.46     | 1.45     | 33.18     | 1.40     | 34.38     | 1.50     | 2.21     |\n| **200:1**    | **32.40** | **0.74** | **46.02** | **1.52** | **35.97** | **1.45** | **37.55** | **1.53** | **3.54** |\n| 100:1        | 28.77     | 0.84     | 49.73     | 1.77     | 31.58     | 1.67     | 38.46     | 1.61     | 5.21     |\n\n**Q4: The memory update mechanism in STMR**\n\nThe Short-Term Memory mechanism involves two distinct phases with different sample selection strategies:\n- During the discovery phase, we adopt a simple FIFO policy to quickly accumulate samples of newly discovered unknown classes. This enables the memory to capture temporal knowledge in real time, facilitating prototype initialization and early-stage adaptation.\n- During the correction (renewal) phase, we use random sampling from the memory to update prototypes and adapt DAA. This strategy avoids bias toward any specific samples and supports stable online learning.\n\nWe chose random sampling in the correction phase primarily for efficiency and robustness. As shown below, the average confidence of unknown samples improves over time, suggesting that early uncertainty does not necessarily reflect long-term reliability:\n\n| Unknown samples   | Average Maximum Prediction confidence of Unknown classes samples with prototypes |\n| ----------------- | ------------------------------------------------------------ |\n| Before test phase | 0.652                                                        |\n| After test phase  | 0.755                                                        |\n\nThis indicates that filtering based on early confidence scores could be both ineffective and computationally expensive, as low-confidence samples may become more reliable later. While more sophisticated filtering strategies (e.g., confidence-based selection or uncertainty estimation) are promising directions, our current design prioritizes simplicity and has proven effective in practice. We leave more advanced selection policies for future exploration."}, {"review_id": "7fuOYZgvSk", "reviewer": "Reviewer_u5r4", "summary": "This paper proposes a novel approach to test-time discovery - the task of detecting and adapting to previously unseen categories during inference. This problem lies at the intersection of test-time training and novel category discovery. The core contribution is the introduction of the Discrepancy-Amplifying Adapter (DAA), which combines a trainable feature transformation with a short-term memory renewal strategy (STMR). The first component employs a warm-up strategy with self-supervised learning to regularize the feature embedding space, encouraging the clustering of samples within the same category. The second component incorporates a memory bank that stores representative samples for replay, effectively preserving knowledge of previously seen categories and mitigating catastrophic forgetting when performing test-time training.", "questions": "1. I suggest that the authors address the questions in the strengths and weaknesses sections.\n2. Regarding checklist item 3, the authors state that 'We provided the assumptions and proof.' However, the submitted paper and  supplemental materials do not include any assumptions or theorems.", "limitations": "The limitations of this work are briefly described at the end of the conclusion section. A more detailed discussion on potential strategies to address these challenges would strengthen the paper. The authors should also discuss other limitations, such as using the memory queue at test-time limits the real-world adoption, and the extra training might impose computational overhead compared to training-free approaches. It would be valuable to assess whether the observed accuracy gains justify the increased computational cost.", "rating": 3, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 1, "significance": 2, "originality": 2, "strengths_and_weaknesses": "**Strengths:** The derivation from training-free in the TTD task appears reasonable, and the extensive evaluations on benchmark datasets provide some evidence supporting the effectiveness of the proposed approach.\n\n**Weaknesses:**  \n1. Overall, the paper requires significant revisions, as the current writing makes it difficult to follow and understand. As a result, it is hard to assess the technical contributions of this paper effectively.\nThe sim( ) operator on line 181 is undefined.\nIn Equation 2, what is $x_k$? \nThe captions for figures should be self-contained; for example, what are the units and meaning of the x and y axes?\nEquation 7 is confusing. Why is there a $L_{kn}$ term inside the definition of $L_{kn}$; similar for $L_{un}$? \nWhat does “real-time” eval and “post-eval” rigorously mean? There are some descriptions in the caption of Table 1, but they seem to be unclear.\n\n2. There are several concerns about the clarity of the proposed method that may affect the reproducibility of this work:\nThe choice of $\\gamma$ is non-trivial. How did the authors select this value? A sensitivity analysis for this parameter should be provided.\nIn Line 141, the loss function is described as a 'weighted combination'; however, Equation 3 does not include any coefficients, implying that the two loss terms are equally weighted.\nWhat is the architecture of DAA?\nWhat is the typical size of the memory queue?\n\n3. The objective of the warm-up stage requires further justification. In Equation 1, do $x$ and $x’$ come from two different categories? If they are randomly selected from one category, they should not create any unknown category. For Eq 2, why is MSE used, while the contrastive loss can still be used if we consider $DAA(r(x))$ and $r(x)$ is a “positive-pair” in the regular contrastive loss?\n\n4. For the  STMR, how to decide when the renewal step is performed. In line 161, “it will be triggered every several batches,” which seems to be unclear.", "was_revised": true, "final_justification": "We appreciate the authors’ significant efforts in providing numerous corrections and additional ablation studies. It is recommended that all these updates be incorporated into the final version. Accordingly, I will raise my score. \n\nNevertheless, I still find the overall approach overly complicated. While sensitivity analyses are provided, some design choices seem questionable - for example, the ratios for combining two losses are given as 1000:1, 500:1, and 1000:2. The last ratio is effectively identical to 500:1 and minimizing L versus 2L with gradient descent should yield the same result. Another note: while the ablation studies are included, the proposed approach appears complex, with many hyperparameters and value choices, which may make it challenging to tune effectively and adopt in practice.", "rebuttal": "Thank you very much for raising these important review points.\n\n### Weakness\n\n**W1**\n\nDue to space constraints, we have omitted some parts. We will rearrange the article and add some descriptions in the main text to make it clearer.\n\n- `sim()` denotes cosine similarity between two vectors:\n$\\text{sim}(\\mathbf{a}, \\mathbf{b}) = \\frac{\\mathbf{a} \\cdot \\mathbf{b}}{\\|\\mathbf{a}\\| \\|\\mathbf{b}\\|}$\n- About Eq.2, we further modified the equation to avoid confusion and increase readability: $\\mathcal{L}\\_ {\\text{un}}(x) = -\\log \\frac{\\exp(\\sigma[\\text{DAA}(\\mathbf{\\tilde{r}}(x)),\\text{DAA}(\\mathbf{\\tilde{r}}(x))\\_ {aug}])}{\\sum\\_ {x' \\in \\mathcal{B}} \\exp(\\sigma[\\text{DAA}(\\mathbf{r}(x')), \\text{DAA}(\\mathbf{\\tilde{r}}(x'))])}$, where\nx' iterates over all samples in the mini-batch $\\mathcal{B}$ including known classes samples and simulated unknown samples. $\\text{DAA}(\\mathbf{\\tilde{r}}(x))\\_{aug}$ is the feature obtained through data augmentation operations.\n- Captions for figures: We will expand figure captions to clarify axes, units, and color meanings. For example, in Fig. 4: x-axis = methods, y-axis = agreement (%), purple = TA, red = CA.\n- In Equation (7),  $\\mathcal{L}\\_ {\\text{kn}}$ and $\\mathcal{L}\\_ {\\text{un}}$ represents batch level calculation. \n\n- “real-time” eval and “post-eval”: We follow HM and define both clearly. Real-time eval reflects online performance during streaming; post-eval re-evaluates all samples after testing completes.\n\n**W2: Implementation details**\n\nThank you for pointing out the reproducibility-related concerns. We address each part below and will clarify these points in the main text or appendix.\n\n\n**W2(a): The choice of $\\gamma$**\n\n$\\gamma$ was selected through grid search in our experiment, and we analyzed the impact of $\\gamma$ on a single dataset in the Appendix. The typical threshold of $\\gamma$ is 0.7. And our method is not sensitive to selection from one dataset to other datasets. We conducted the following experiments: \n\n| Dataset | $\\gamma$ | TA(realtime) | CA(realtime) | KF   |\n| - | - | - | - | - |\n| Cifar100-D | 0.6 | 20.04   | 48.07   | 4.43 |\n| | 0.7 | 32.40   | 46.02   | 3.54 |\n| | 0.8 | 29.90   | 42.78   | 7.96 |\n| CUB-D | 0.6 | 45.66   | 54.39   | 5.55 |\n| | 0.7 | 54.45   | 58.62   | 3.62 |\n| | 0.8 | 46.29   | 55.85   | 7.22 |\n| Tiny-ImageNet-D | 0.6 | 18.82   | 69.17   | 4.55 |\n| | 0.7 | 24.36   | 71.02   | 2.34 |\n| | 0.8 | 24.08   | 65.44   | 8.26 |\n\n**W2(b) Weight of two loss in Eq.(3)**\n\nThe weight of two loss during warm-up phase is fixed (200:1 in paper).\nWe will add the weight parameters to avoid assuming a 1:1 ratio, and we will include different comparison results as below.  We have provieded different ratio of weight parameters during warm-up in the appendix, and we also show it as follows.\n\n| MSE:Contrast | Real | -   | time | Eval | Post |  |   | Eval |  |\n| - | - | - | - | - | - | - | - | - | - |\n| | TA   | TE  | CA   | CE  | TA   | TE  | CA   | CE  | KF  |\n| 1000:1  | 27.89 | 0.68 | 44.46 | 1.45 | 33.18 | 1.40 | 34.38 | 1.50 | 2.21 |\n| **200:1** | **32.40** | **0.74** | **46.02** | **1.52** | **35.97** | **1.45** | **37.55** | **1.53** | **3.54** |\n| 100:1   | 28.77 | 0.84 | 49.73 | 1.77 | 31.58 | 1.67 | 38.46 | 1.61 | 5.21 |\n\nDuring testing phase, we have discussed the influence of weight in appendix. And the results is also shown in the following table.\n\n| MSE:Contrast | Real | -   | time | Eval | Post |  |   | Eval |  |\n| - | - | - | - | - | - | - | - | - | - |\n| | TA   | TE  | CA   | CE  | TA   | TE  | CA   | CE  | KF  |\n| 1000:0.1 | 28.41 | 0.80 | 44.91 | 1.56 | 29.50 | 1.49 | 30.53 | 1.74 | 0.92 |\n| 1000:1  | 33.66 | 0.66 | 49.81 | 1.39 | 34.8 | 1.03 | 37.23 | 1.48 | 2.64 |\n| **1000:2**   | **32.40** | **0.74** | **46.02** | **1.52** | **35.97** | **1.45** | **37.55** | **1.53** | **3.54** |\n| 500 : 1 | 30.20 | 0.84 | 42.64 | 1.64 | 33.30 | 1.48 | 34.37 | 1.71 | 3.76 |\n\nDuring both warm-up and testing phase, the MSE loss is used to refine the model's predictions for known classes, while the contrastive loss encourages the model to separate the feature embeddings of known and unknown classes. When the model places a higher emphasis on known classes, the model's ability to distinguish between known and unknown classes is limited. As the ratio decreases, the model starts to pay more attention to the contrastive loss, which helps improve the separation between known and unknown classes. \n\n\n\n**W2(c) Architecture of DAA**\n\nThe DAA adopts a standard 2-layer adapter [1] inserted after the frozen ViT-B/16 backbone. It consists of:\n-\tLinear down-projection: 768 → 128\n-\tReLU activation\n-\tLinear up-projection: 128 → 768\n\nThis design introduces minimal overhead (∼200K parameters).\nWe emphasize that the adapter structure itself is not our contribution.\nIt follows a widely used design.\nOur key innovation lies in the training strategy that leverages this lightweight adapter to retain known-class features and amplify discrepancies for unknowns, enabling effective test-time discovery.\nWe will add the architectural details to the appendix for clarity.\n\n[1]Houlsby, Neil, et al. \"Parameter-efficient transfer learning for NLP.\" International conference on machine learning. PMLR, 2019.\n\n**Q2(d) Typical size of the memory queue**\n\nThe typical size of the memory queue is 10 as we analyze different memory size in terms of agreement and time consumption **in Figure 6 in the main paper**.  While larger memory can store more samples, it may include outdated or irrelevant data, weakening prototype quality. Conversely, too small a memory may fail to capture representative features, reducing generalization. Additionally, increasing memory size leads to higher computational cost. \n\n**W3(a): Do x and x' come from two different categories**\n\n**The samples x are from the set of known classes and x' are created by Mixup-Gaussian augmentation on other samples in the batch**. These simulation method **is widely used** in many work, such as OpenMix [2] and HiLo [3]. Although the mixture do not produce a real unknown category, they can effectively simulate unknown classes.\n\nWe conducted experiments and the results showed that the average confidence of **simulated samples was lower than that of known class samples** (lower confidence is easier to be discovered); The average confidence level of the truly unknown samples obtained by our method is also lower than that of the unknown samples under the HM method. Similarly, our method has a higher success rate than the HM method in directly discovering new classes through threshold method.\n\n|   | Average Maximum Prediction confidence with known prototypes | Percentage of categories have been classified /  discovered |\n| - | - | - |\n| Simulated unknown classes | 0.608  | - |\n| Unknown classes of DAA | 0.652  | 52.63 % (Threshold-discover directly)  |\n| Unknown classes of HM | 0.705  | 46.38 % (Threshold-discover directly)  |\n\n[2]Zhong, Zhun, et al. \"Openmix: Reviving known knowledge for discovering novel visual categories in an open world.\" Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021.\n\n[3]Wang, Hongjun, Sagar Vaze, and Kai Han. \"Hilo: A learning framework for generalized category discovery robust to domain shifts.\" arXiv preprint arXiv:2408.04591 (2024).\n\n**W3(b): For Eq 2, why is MSE used and how contrastive loss work**\n\nThe MSE keeps the adapter output close to the original backbone feature when the input is a known sample. This acts as a regularizer that prevents forgetting and destruction of the known classes. \nKnown classes features and simulated features as a mini-batch, simulated features and feature augmented simulated features as positive samples, and simulated features and all other data within the small batch as negative samples.\n\n**W4: Frenquency of STMR**\n\nWe made the comparison about frenquency of STMR as a Hyper-parameter analysis in Appendix. **The typical number of frequency in paper is 2**.\n\n| Frenquency | TA | CA | TA+CA | Time   |\n| - | - | - | - | - |\n| 1  | 36.80 | 35.75 | 72.55 | 318.03 |\n| 2  | 35.97 | 37.55 | 73.52 | 293.95 |\n| 5  | 35.80 | 32.94 | 68.74 | 262.19 |\n| Never | 36.37 | 33.11 | 69.48 | 226.64 |\n\nA lower frequency of STMR leads to insufficient updates and refinements of the model during the test phase, resulting in poorer recognition performance for both known and novel classes. While increasing the frequency of STMR can improve the model's ability to adapt to new data, it also leads to a substantial increase in computational overhead. Frequent STMR operations require more time and resources for processing each batch.\n\n### Question & Limitation:\n\n**Q1: Checklist**\n\nThis is a typographical error, and our submission does not contain formal theorems or explicit theoretical assumptions. However, the claims regarding the effectiveness of DAA and STMR are supported by extensive experimental validation across multiple datasets and ablations. For example, Fig. 5 in the main paper and the t-SNE visualization in the main paper, and the appendix validate our effect. \n\n**Q2: Computational cost**\n\nIn terms of time cost, we conducted an experiment on Cifar-100 (70+30) as shown in the following table.\n\n| | Time(s) | Memory Usage(MB) |\n| - | - | - |\n| GMP | 885.59  | 14102 |\n| PHE | 280.52  | 9025 |\n| HM  | 505.55  | 13919 |\n| DAA(Ours) | 293.95  | 10813 |\n \nThe results show that our time consumption is relatively small.\nDAA is efficient: Compared to GMP, which updates the full backbone and incurs heavy overhead, and HM, which performs test-time neighbor search, our approach is both faster and lighter.\nPHE is faster, but it lacks any test-time adaptation and thus sacrifices performance.\nOur memory design uses a short FIFO queue, which is more compact than HM’s bucket/hash-based storage.\nIn conclusion, our method achieves strong performance with only minor overhead, making it practical for real-world usage."}, {"review_id": "xfUCVTH0lY", "reviewer": "Reviewer_u1k8", "summary": "This paper addresses the task of Test-Time Discovery (TTD) the problem of identifying and adapting to novel classes during inference while maintaining performance on known classes, a setting specifically relevant in dynamic environments like autonomous driving and healthcare. While prior approaches to TTD are typically training-free and rely on static model representations (e.g., via frozen backbones and hash-based memory updates), the authors propose a more adaptive and learnable alternative.\n\nTheir core contribution is the Discrepancy-Amplifying Adapter (DAA), a trainable module designed to amplify the feature discrepancies between known and unknown classes during both training and test time. DAA is preconditioned using a warm-up phase that simulates unknown class features via mixup and gaussian noise, and is trained with a dual loss—preserving known-class features via MSE while enforcing contrastiveness for unknowns.\n\nTo handle continual adaptation during test time, the authors introduce a Short-Term Memory Renewal (STMR) mechanism a queue-based dynamic memory system that manages prototypes of both known and discovered unknown classes. STMR periodically refreshes the memory with reliable, updated feature representations and supports DAA's self-supervised test-time adaptation. This setup avoids representational drift and enables the model to remain plastic to novel classes while stable on the known set.\n\nExtensive experiments on CIFAR100, CUB, and Tiny-ImageNet benchmarks demonstrate the proposed method outperforms both training-free and training-based baselines in real-time and post-hoc evaluation across clustering, classification, and forgetting metrics. Ablations further support the complementary roles of DAA and STMR. The method also maintains competitive knowledge retention while improving discriminative capability for new class discovery.", "questions": "#### 1. **Clarify the DAA Adapter Architecture**\n- **What is the architecture used for the DAA adapter?**  \n  Please include a detailed architectural description (e.g., number of layers, type of transformations, parameter count) in the **main paper or appendix**, as this is critical for **reproducibility** and assessing overhead.\n\n#### 2. **Streamline the Contribution Statement**\n- In the **introduction**, consider consolidating the contributions into **two key components**:\n  - The **Discrepancy-Amplifying Adapter (DAA)**  \n  - The **Short-Term Memory Renewal (STMR)**  \n  These are the core innovations driving the improved performance. The SOTA results, while strong, are a consequence of these design choices rather than a standalone contribution.\n\n#### 3. **Clarify Robustness to Noise and Ambiguity**\nPlease explain how your method handles the following scenarios:\n- **Noisy or mislabeled known-class data** that may distort the feature space and lead to false discoveries or prototype pollution.\n- **Early unknown-class samples** that are **unrepresentative or ambiguous**, e.g., outliers, atypical images, or class boundary cases. How resilient is your method to such issues?\n\n#### 4. **Hyperparameter Sensitivity and Tuning Strategy**\nThe method introduces several important hyperparameters:\n- Similarity threshold **γ**\n- Memory size\n- Warm-up duration\n- Loss weights **λ₁** and **λ₂**\n- Mixup beta parameters to generate early unknowns\n\nHowever, there is minimal discussion of how these were selected or how sensitive the model is to their variation.  \nPlease include:\n- A brief **hyperparameter sensitivity analysis**, and\n- Whether the values are transferable across datasets or require tuning per setup.\n\n#### 5. **Justify Memory Design Choices**\nYour memory module is a core component—please expand on the rationale and trade-offs:\n- **Why was FIFO** selected over alternatives such as **exemplar-based sampling** or **confidence-based filtering**?  \n- **How frequently is memory renewal triggered**, and how was this interval chosen? Is it a fixed schedule or adaptive?", "limitations": "Yes, the authors have acknowledged some limitations of their approach in the conclusion specifically the computational cost introduced by the warm-up and adaptation stages, and the heuristic nature of the memory update strategy (STMR), especially under noisy conditions.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 4, "originality": 3, "strengths_and_weaknesses": "## Strengths\n\n### Quality\n\n- Well-Motivated and Technically Sound Solution: The paper addresses a genuinely hard and under-explored problem test-time discovery of novel classes in an online setting by integrating a trainable module (DAA) into a previously training-free setup.\n- Strong Experimental Validation: The authors perform thorough empirical evaluation across three benchmark datasets (CIFAR100D, CUB-200D, Tiny-ImageNetD), using appropriate metrics for both clustering quality and classification robustness (e.g., HCA, ARI, NMI, KA, KF). Both real-time and post-evaluation are considered, and the paper demonstrates consistent superiority over baselines including HM [16] and prompt-based continual learning methods (e.g., GMP, L2P).\n- Solid Ablation and Analysis: The ablation studies are clear and informative, isolating the effects of each component (DAA, STMR, test-time training). \n\n### Clarity\n- Well-Structured Presentation: The paper is clearly written and well-organized. The motivation for each component (DAA, STMR) is carefully laid out with visual illustrations (e.g., Fig. 1b, Fig. 2, Fig. 3). \n- Clear Definitions and Objectives: The formulation of the TTD task is precise, with explicit delineation from closely related paradigms like TTA and NCD. \n\n### Significance\n- Addressing a Critical Gap in TTD: The paper goes beyond the limitations of training-free test-time methods (which rely on frozen backbones and static prototypes) and introduces a practical, adaptive framework. This directly advances the state of the art in TTD, a task that is gaining traction for real-world applications in robotics and healthcare.\n- Potential for Broader Impact: The architectural idea of plug-in adapters for open-world adaptation could generalize to other settings such as continual learning.\n\n### Originality\n- Novel Adapter Design for TTD: While adapters and test-time training are known concepts, their use in the open-world discovery setting is novel.\n- STMR as a Lightweight Memory Mechanism: The Short-Term Memory Renewal (STMR) module is a notable contribution that balances model plasticity and stability, improving prototype quality without incurring the long-term errors seen in training-free memory-based approaches.\n\n## Weaknesses\n\n- Hyperparameter Sensitivity Unclear: The performance of DAA and STMR depends on hyperparameters (e.g., warm-up length, memory size, threshold γ, weighting factors λ₁ and λ₂). Although some sensitivity is analyzed (Fig. 6), a more comprehensive exploration would strengthen claims of robustness.\n-  Assumption of Class-Purity in Early Unknowns: The method assumes that initial unknown class predictions can seed meaningful prototypes. This assumption might not hold in highly noisy or fine-grained scenarios, leading to potential error accumulation in STMR. For example if the original dataset is contaminated with noisy data will this method be still competitive with methods based on hash lists and static representations.\n- Key implementation details are missing (e.g., architecture of DAA, training dynamics, runtime performance). Some are essential for reproducibility and could have been briefly summarized in the main paper.", "was_revised": true, "final_justification": "The rebuttal clarified the DAA architecture, provided robustness experiments under label/image noise, and expanded hyperparameter sensitivity analysis, addressing key concerns about reproducibility and brittleness. Results remain strong and consistent across datasets, and the technical contribution is well-motivated. However, cross-dataset hyperparameter robustness is still limited, some training dynamics and computational trade-offs remain only partially described, and the assumption of class purity in early unknowns is only partially mitigated. Overall, the paper is technically sound with clear novelty and solid empirical gains, but with remaining uncertainties about generalization to more challenging/noisy open-world settings. I maintain a score of  5(Accept).", "rebuttal": "**Q1: Architecture of DAA**\n\nThe DAA adopts a standard 2-layer adapter [1] inserted after the frozen ViT-B/16 backbone. It consists of:\n- Linear down-projection: 768 → 128\n- ReLU activation\n- Linear up-projection: 128 → 768\n\nThis design introduces minimal overhead (∼200K parameters).\nWe emphasize that the adapter structure itself is not our contribution.\nIt follows a widely used design.\nOur key innovation lies in the training strategy that leverages this lightweight adapter to retain known-class features and amplify discrepancies for unknowns, enabling effective test-time discovery.\nWe will add the architectural details to the appendix for clarity.\n\n[1]Houlsby, Neil, et al. \"Parameter-efficient transfer learning for NLP.\" International conference on machine learning. PMLR, 2019.\n\n**Q2: Advice for Contribution**\n\nThank you for the suggestion. We agree that the core contributions lie in DAA and the Short-Term Memory Renewal (STMR), and we will revise the introduction to reflect this more clearly.\nThe reported performance gains serve as empirical validation of these two design components, rather than standalone contributions. We appreciate the feedback and will streamline the contribution statement accordingly.\n\n**Q3: Robustness to Noise**\n\nThank you for raising this important concern. \nTo evaluate the robustness of our method under such conditions, we conducted a series of experiments introducing both **label noise** and **image noise** at a 10% corruption rate, under different stages (warm-up and test-time).\n\n| | Pre-stage | Real-time | Eval | Post | Eval |  |\n| - | - | - | - | - | - | - |\n| | KA   | TA+CA | TE+CE | TA+CA | TE+CE | KF  |\n| label noise(10%, Warm-up)(Ours) | 78.85 | 76.13 | 2.55 | 67.81 | 3.07 | 3.92 |\n| image noise(10%, Warm-up)  | 81.06 | 76.87 | 2.46 | 68.53 | 3.02 | 3.99 |\n| image noise(10%, Test-time)   | 84.20 | 75.46 | 2.49 | 65.91 | 3.15 | 5.23 |\n| **No noise**  | **84.20** | **78.42** | **2.26** | **73.52** | **2.98** | **3.54** |\n| **HM** label noise(10%, Training)  | 75.24 | 70.13 | 2.67 | 65.02 | 3.20 | 5.11 |\n| **HM** image noise(10%, Training)  | 77.18 | 71.54 | 2.62 | 66.31 | 3.14 | 5.00 |\n| **HM** image noise(10%, Test-time) | 84.13 | 68.15 | 3.22 | 60.46 | 3.35 | 7.46 |\n\nDespite the noise, our method consistently outperforms the baseline HM in both real-time and post evaluations. The results suggest that our design, especially the FIFO-based memory buffer and STMR’s error correction mechanism—provides substantial robustness and stabilizing effects under noisy conditions.\n\n**Q4: Hyperparameters**\n\n**Q4(a): Similarity threshold $\\gamma$**\n\n$\\gamma$ was selected through grid search in our experiment, and we analyzed the impact of $\\gamma$ on a single dataset in the Appendix. **The typical threshold of $\\gamma$ is 0.7.** And our method is **not sensitive to selection from one dataset to other datasets**. We conducted the following experiments: \n\n\n| Dataset | $\\gamma$ | TA(realtime) | CA(realtime) | KF   |\n| - | - | - | - | - |\n| Cifar100-D | 0.6 | 20.04   | 48.07   | 4.43 |\n| | 0.7 | 32.40   | 46.02   | 3.54 |\n| | 0.8 | 29.90   | 42.78   | 7.96 |\n| CUB-D | 0.6 | 45.66   | 54.39   | 5.55 |\n| | 0.7 | 54.45   | 58.62   | 3.62 |\n| | 0.8 | 46.29   | 55.85   | 7.22 |\n| Tiny-ImageNet-D | 0.6 | 18.82   | 69.17   | 4.55 |\n| | 0.7 | 24.36   | 71.02   | 2.34 |\n| | 0.8 | 24.08   | 65.44   | 8.26 |\n\n\n**Q4(b): Memory size**\n\nWe have analyzed different memory size in terms of agreement and time consumption **in Figure 6 in the manuscript**. And the typical size of the memory queue is 10. \nWhile larger memory can store more samples, it may include outdated or irrelevant data, weakening prototype quality. Conversely, too small a memory may fail to capture representative features, reducing generalization. Additionally, increasing memory size leads to higher computational cost. \nWe also analyze the size choice around 10. The results indicate that small differences around 10 have little impact on the effectiveness. \n\n| | TA | CA | TA+CA |\n| - | - | - | - |\n| 9 | 35.86 | 37.73 | 72.55 |\n| 10   | 35.97 | 37.55 | 73.52 |\n| 11   | 35.38 | 37.69 | 68.74 |\n\n**Q4(c): Warm-up duration**\n\nFew warm-up epochs result in insufficient effectiveness, but when the number of rounds reaches a certain level, the effect tends to converge and the improvement is limited.\n\n| Epochs | KA | Average Maximum Prediction confidence of Unknown classes samples with Known prototypes |\n| - | - | - |\n| 20 | 80.56 | 0.684   |\n| 50 | 84.20 | 0.652   |\n| 100 | 84.16 | 0.650   |\n| 150 | 84.23 | 0.648   |\n\n\n**Q4(d): Loss weights**\n\nThe weight of two loss **during warm-up phase is fixed (200:1 in paper)**, and we also tried other combinations as shown in the table below. \n\n| MSE:Contrast | Real | -   | time | Eval | Post |  |   | Eval |  |\n| - | - | - | - | - | - | - | - | - | - |\n| | TA   | TE  | CA   | CE  | TA   | TE  | CA   | CE  | KF  |\n| 1000:1  | 27.89 | 0.68 | 44.46 | 1.45 | 33.18 | 1.40 | 34.38 | 1.50 | 2.21 |\n| **200:1** | **32.40** | **0.74** | **46.02** | **1.52** | **35.97** | **1.45** | **37.55** | **1.53** | **3.54** |\n| 100:1   | 28.77 | 0.84 | 49.73 | 1.77 | 31.58 | 1.67 | 38.46 | 1.61 | 5.21 |\n\nDuring testing phase, we have discussed the influence of weight in the appendix, and we also show the resulst in the table below.\n\n| MSE:Contrast | Real | -   | time | Eval | Post |  |   | Eval |  |\n| - | - | - | - | - | - | - | - | - | - |\n| | TA   | TE  | CA   | CE  | TA   | TE  | CA   | CE  | KF  |\n| 1000:0.1 | 28.41 | 0.80 | 44.91 | 1.56 | 29.50 | 1.49 | 30.53 | 1.74 | 0.92 |\n| 1000:1  | 33.66 | 0.66 | 49.81 | 1.39 | 34.8 | 1.03 | 37.23 | 1.48 | 2.64 |\n| **1000:2**   | **32.40** | **0.74** | **46.02** | **1.52** | **35.97** | **1.45** | **37.55** | **1.53** | **3.54** |\n| 500 : 1 | 30.20 | 0.84 | 42.64 | 1.64 | 33.30 | 1.48 | 34.37 | 1.71 | 3.76 |\n\n\n**Q4(e): Mixup beta parameters**\n\nIn our implementation of the Mixup strategy,  we conducted Symmetric Beta Distribution, and the default Beta parameters is 0.5. And we also conduct the hyperparameter sensitivity analysis.\n\n| $\\beta$ | Average Maximum Prediction confidence of Unknown classes samples with Known prototypes |\n| - | - |\n| 0.1 | 0.762   |\n| 0.49 | 0.657   |\n| 0.5 | 0.652   |\n| 0.51 | 0.654   |\n| 0.9 | 0.744   |\n\nThe small difference in Beta parameters of Mixup around 0.5 leads to a similar effect in final discrimination ability. Mixing situations that are too high or too low can result in poor simulation performance\n\n**Q5(a): Why was FIFO**\n\n\nThank you for the question. In our design:\nThe FIFO is used in the discovery phase and the random sampling is used in the correction phase.\nThe sample queue can accumulate knowledge of new classes in a timely manner during the discovery phase, but it may introduce errors, so the correction phase uses random sampling for correction.\nTo ensure efficiency, we have found that random sampling is already good and may consider better solutions in the future, such as selecting samples with higher uncertainty.\n\n| Unknown samples   | Average Maximum Prediction confidence of Unknown classes samples with prototypes |\n| - | - |\n| Before test phase | 0.652   |\n| After test phase  | 0.755   |\n\nDue to the fact that the confidence level of new samples may be low when discovered. As the testing phase progresses, the samples with low confidence level at the time of discovery may have actually become higher. \n\n**Q5(b): Frenquency of STMR**\n\nThank you for the question. We treat the STMR renewal frequency as a tunable hyperparameter, and have reported a comparative analysis in the Appendix.\nIn our main experiments, we set the renewal frequency to 2 batches, which provides a good trade-off between performance and efficiency:\n\n| Frenquency | TA | CA | TA+CA | Time   |\n| - | - | - | - | - |\n| 1  | 36.80 | 35.75 | 72.55 | 318.03 |\n| 2(default)  | 35.97 | 37.55 | 73.52 | 293.95 |\n| 5  | 35.80 | 32.94 | 68.74 | 262.19 |\n| Never | 36.37 | 33.11 | 69.48 | 226.64 |\n\nA lower frequency of STMR leads to insufficient updates and refinements of the model during the test phase, resulting in poorer recognition performance for both known and novel classes.While increasing the frequency of STMR can improve the model's ability to adapt to new data, it also leads to a substantial increase in computational overhead. Frequent STMR operations require more time and resources for processing each batch. \nWe currently use a fixed schedule, but exploring adaptive strategies based on confidence or drift detection is a promising direction for future work."}, {"review_id": "NYQvC96xcO", "reviewer": "Reviewer_VhXu", "summary": "This paper proposes a Discrepancy-Amplifying Adapter (DAA) method to enhance the Test-Time Discovery task. Specifically, the DAA module amplifies feature discrepancies between known and unknown classes for test-time adaptation. Besides, a Short-Term Memory Renewal (STMR) mechanism is employed to ensures effective prototype management through adaptive memory updates. Extensive experiments demonstrate its superiority in adaptability and stability, improving novel class discovery performance.", "questions": "1.When simulating unknown classes, this method employs an interpolation of two different known classes as described Eq.1. My concern is that the simulated class still within the range of known distributions. Will the method remain effective when dealing with unknown classes that lie outside these distributions?\n\n2. Can the proposed method be extended to address continual learning scenarios?\n\n3.It is suggested to provide an overall algorithm for clearer illustration.", "limitations": "See weakness.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n1.This work introduces a trainable DAA module in the training phase, which simulates unknown categories and enhance the model’s discriminative ability.\n\n2.This paper proposes a STMR mechanism in the testing phase, dynamically updating stored representations through a queue mechanism to support prototype updates, sample replay, and test-time fine-tuning of DAA. \n\n3.Comprehensive evaluation across multiple datasets show significant improvements over existing methods in both real-time and post evaluations.\n\nWeaknesses:\n\n1.The heuristic-based memory update strategy may not perform optimally in highly noisy environments, potentially limiting its effectiveness.\n\n2.The evaluation benchmarks are relatively small in scale, which may be insufficient to fully demonstrate the method's effectiveness.", "was_revised": true, "final_justification": "Thanks for the response and the rebuttal, which addresses my concerns and I will raise my score accordingly.", "rebuttal": "**Q1: Effectiveness to Noise**\n\nThank you for raising this important concern. \nTo evaluate the robustness of our method under such conditions, we conducted a series of experiments introducing both **label noise** and **image noise** at a 10% corruption rate, under different stages (warm-up and test-time).\n\n| | Pre-stage | Real-time | Eval | Post | Eval |  |\n| - | - | - | - | - | - | - |\n| | KA   | TA+CA | TE+CE | TA+CA | TE+CE | KF  |\n| label noise(10%, Warm-up)(Ours) | 78.85 | 76.13 | 2.55 | 67.81 | 3.07 | 3.92 |\n| image noise(10%, Warm-up)  | 81.06 | 76.87 | 2.46 | 68.53 | 3.02 | 3.99 |\n| image noise(10%, Test-time)   | 84.20 | 75.46 | 2.49 | 65.91 | 3.15 | 5.23 |\n| **No noise**  | **84.20** | **78.42** | **2.26** | **73.52** | **2.98** | **3.54** |\n| **HM** label noise(10%, Training)  | 75.24 | 70.13 | 2.67 | 65.02 | 3.20 | 5.11 |\n| **HM** image noise(10%, Training)  | 77.18 | 71.54 | 2.62 | 66.31 | 3.14 | 5.00 |\n| **HM** image noise(10%, Test-time) | 84.13 | 68.15 | 3.22 | 60.46 | 3.35 | 7.46 |\n\nDespite the noise, our method consistently outperforms the baseline HM in both real-time and post evaluations. The results suggest that our design, especially the FIFO-based memory buffer and STMR’s error correction mechanism—provides substantial robustness and stabilizing effects under noisy conditions.\n\n\n\n**Q2: Insufficient benchmark**\n\nWe appreciate the reviewer’s suggestion regarding the scale of evaluation benchmarks. While we agree that validating on larger or more diverse datasets is an important direction, we would like to emphasize that TTD is already a highly challenging task even on the current benchmarks.\nFor instance, on Tiny-ImageNet-D—a dataset with 200 classes and significant visual diversity—discovering novel classes without any supervision remains extremely difficult. As shown in Figure 4, our method achieves only 24.10% average true-label agreement (TA), highlighting how hard it is to consistently cluster unseen class samples even with a well-trained backbone. This indicates that achieving reliable TTD performance on existing benchmarks is already non-trivial.\nTherefore, while scaling up is valuable, we believe that pushing the performance boundary on these already-challenging benchmarks is equally, if not more, important at the current stage. We appreciate the suggestion and plan to extend our evaluation to more complex datasets in future work.\n\n**Q3:  Effectiveness of simulated samples** \n\nThank you for the insightful question. To evaluate the effectiveness of our method under such conditions, we conducted a series of statistical analyses and comparative experiments.\n\n**Q3(a): maximum matching confidence statistic**\n\nSince our method discovers new classes based on maximum matching confidence, we analyzed the confidence scores of different sample types with respect to known-class prototypes. The results are summarized below:\n\n|   | Average Maximum Prediction confidence with known prototypes | Percentage of  discovered categories  by threshold-discover directly |\n| - | - | - |\n| Simulated unknown classes | 0.608  | -  |\n| Unknown classes of DAA | 0.652  | 52.63 % |\n| Unknown classes of HM | 0.705  | 46.38 % |\n\nThese findings suggest that:\n- Simulated samples occupy more ambiguous regions in the feature space, with lower confidence compared to known-class samples.\n- Real unknowns discovered by our method are also harder (lower confidence) than those handled by HM, indicating better modeling of the decision boundary.\n-\tOur method achieves a higher direct discovery success rate via thresholding, showing its superior ability to identify novel categories in ambiguous zones.\n\n**Q3(b): Effectiveness on samples lie outside known distributions**\n\n\nTo examine our method’s behavior for unknown classes that lie far outside the known distribution, we introduced additional noise to these samples, making them more separable. Results are shown below:\n\n| Testing phase | Average Maximum Prediction confidence with known prototypes | Percentage of  discovered categories  by threshold-discover directly |\n| - | - | - |\n| Unnoised Unknown classes   | 0.652  | 52.63%  |\n| **Noised** Unknown classes | 0.634  | 54.97 % |\n\nThe experiment shows that more distinct unknowns (i.e., farther from the known distribution) tend to have lower confidence and are easier to discover. This confirms that our method performs well in such cases even without simulation, as these samples are less ambiguous.\n\n\nIn summary, our simulation focuses on the most difficult unknowns—those near the known distribution, where class confusion is high. These are the scenarios where explicit modeling is most needed. Conversely, unknowns that lie far outside the known space are easier to identify and do not require sophisticated simulation to be discovered. This demonstrates the practical value and effectiveness of our simulated unknown training strategy.\n\n\n**Q4: Extend to Continual Learning scenarios**\n\nOur method **can naturally generalizes to continual-learning settings**. And to verify this compatibility, we conducted an experiment that follows the setting of continual stage in Continual Class Discovery (CCD) scenarios.\nWe conducted the preliminary 3-stages experiment on Cifar-100 (70+10+10+10)  \n\n| | Stage | 1（70+10） |  | Stage | 2 (70+10+10)  |  | Stage | 3 | (70+10 | +10+10)   |  |\n| - | - | - | - | - | - | - | - | - | - | - | - |\n| | TA(Real-time) | CA(Real-time) | KF  | TA(Real-time) | CA(Real-time) | KF  | TA(Real-time) | CA(Real-time) | TA(Post)  | CA(Post)  | KF  |\n| GMP | 33.52 | 50.57 | 1.88 | 28.49 | 49.10 | 4.22 | 25.43 | 43.11 | 30.24 | 30.88 | 8.96 |\n| HM | 33.46 | 48.22 | 1.44 | 29.18 | 52.46 | 2.69 | 27.31 | 43.52 | 30.15 | 34.38 | 4.53 |\n| **DAA** | **37.20** | **56.31** | **1.25** | **35.44** | **48.90** | **2.11** | **33.35** | **45.69** | **34.88** | **36.47** | **3.66** |\n\nThe results show that our framework is well-suited to continual class discovery, and these early results show promising potential for broader continual learning applications. Further exploration with more stages and longer task horizons is an exciting direction for future work.\n\n**Q5: Overall algorithm**\n\nTo improve clarity, we provided an overall algorithm as follow.\n\n```\nAlgorithm: DAA Training and Test-Time Discovery with STMR\n\nInput:  \n- Training set $ \\mathcal{D}_{\\text{train}} = \\{(x_i, y_i)\\}_{i=1}^{N_{\\text{train}}} $ with known classes $ \\mathcal{Y}_{\\text{kn}} $  \n- Test stream $ \\mathcal{D}_{\\text{test}} $ with unknown classes  \n- Backbone model $ f $ (frozen), adapter $ \\text{DAA} $ (trainable)  \n- Hyperparameters: warm-up epochs $ E $, memory size $ M $, confidence threshold $ \\gamma $  \n\nOutput:  \n- Predictions $ \\hat{y} $ for test samples, dynamically updated DAA and prototypes  \n\n\n#Phase 1: Discrepancy-Amplifying Warm-up (Pre-Testing)\n\n# Initialize DAA parameters θ_DAA\nfor epoch in range(E):\n for batch B in D_train:\n   # Simulate unknown features via Mixup + Gaussian noise\n   r = f(B)  # Backbone features\n   r_tilde = mixup(r) + N(0, Σ)  # Synthetic unknowns\n   \n   # Compute losses\n   L_kn = MSE(DAA(r), r)  # Preserve known semantics\n   L_un = contrastive_loss(DAA(r), DAA(r_tilde))  # Amplify discrepancies\n   L_train = L_kn + λ * L_un\n   \n   # Update θ_DAA via SGD\n   θ_DAA ← θ_DAA - η * ∇L_train\n\n#Phase 2: Test-Time Discovery with STMR\n\n# Initialize memory queues\nS_kn, F_kn = load_known_prototypes(D_train)  # Static for known classes\nS_un, F_un = {}, {}  # FIFO queues for unknown classes (empty initially)\n\nfor batch B in D_test:\n # Extract features\n r = f(B)\n z = DAA(r)  # Adapted features\n \n # Prediction via prototype matching\n P = cosine_similarity(z, prototypes=[p_c for c in Y_kn ∪ Y_seen])\n y_hat = argmax_c P(c)\n \n # Confidence-based discovery\n if max(P) > γ:\n   assign y_hat  # Known/seen class\n else:\n   y_hat = \"new_unknown\"\n   Y_seen.add(y_hat)\n   initialize p_y_hat = mean(z)  # New prototype\n \n # STMR: Memory renewal\n if y_hat in Y_un:\n   # Renewal step every T batches\n   if batch_id % T == 0:\n for x in S_un[y_hat]:\n   z_renew = DAA(f(x))\n   if prediction(z_renew) ∈ Y_kn:\n discard x  # Remove misclassified known samples\n   else:\n F_un[y_hat].enqueue(z_renew)  # Update features\n   \n   # FIFO update\n   S_un[y_hat].enqueue(B)\n   F_un[y_hat].enqueue(z)\n \n # Test-time training\n L_test = compute_loss(z, y_hat, S_un, F_un)  # Eq. (6) in paper\n θ_DAA ← θ_DAA - η * ∇L_test  # Self-supervised update\n\n```"}]

Author rebuttal:

Meta-review: This paper proposes an adaptable module that improves test-time discovery of novel classes by performing test-time adaptation, with the aim of increasing the difference between novel and already known classes in (hidden) feature space.  This improves upon existing methods that often assume a fixed model.  The reviewers were generally in favor of acceptance (2 x accept, 1 x borderline accept, 1 x borderline reject), with the most critical reviewers taking issue with the paper's clarity, questioning if the method's degree of complexity is warranted, and finding the approach fairly heuristic in nature.  However, the positive reviewers found the paper to be interesting, novel in its approach, and to have substantial experiments that validate the approach.  After reading all discussions and the paper itself, I find the paper to be above the bar in clarity (although could certainly be improved in places, which the authors have promised / shown below) and to be sufficiently well motivated and experimentally validated.
Decision: Accept (poster)