Activate and Adapt: A Two-Stage Framework for Open-Set Model Adaptation
Authors: Xiasi Wang
Jiaqi Lin
Chaoqi Chen
Luyao Tang
Yi Huang
Chengsen Wang
Lei YE
Yuan Yao
Venue: Accepted by TMLR ()

Abstract: The ability of generalizing to new environments is critical for deep neural networks. Most existing works presume that the training and test data share an identical label set, overlooking the potential presence of new classes in test data. In this paper, we tackle a practical and challenging problem: Open-Set Model Adaptation (OSMA). OSMA aims to train a model on the source domain, which contains only known class data, and then adapt the trained model to the distribution-shifted target domain to classify known class data while identifying new class data. In this context, we face two challenges: (1) enabling the model to recognize new classes using only the known class data from the source domain during training, and (2) adapting the source-trained model to the target domain that contains new class data. To address these challenges, we propose a novel and universal two-stage framework named Activate and Adapt (ADA). In the training stage, we extract potential new class information hidden within the rich semantics of the source domain data to enable the model to identify new class data. Additionally, to retain source domain information while preserving data privacy, we condense the source domain data into a small dataset, facilitating the subsequent adaptation phase. In the test stage, we adaptively adjust the source-trained model to the target domain with new classes by infusing the style of target data into the condensed dataset, and decoupling domain alignment for known and new classes. Experiments across three standard benchmarks demonstrate that ADA surpasses previous methods in both online and offline settings.

Published in Transactions on Machine Learning Research (07 (025))
Activate and Adapt:
A Two-Stage Framework for Open-Set Model Adaptation
Xiasi ;ang'; Jiaqi Lin'; Chaoqi Chen Luyao Tang", Yi Huang", Chengsen Wang", Lei Ye', Yuan Yao!
The Hong Kong Universily of Science and Technology Shenzhen University "The University of Hong Kong
Tsinghua University
University of Chinese Academy of Sciences Huawei
Beijing University of Posts and Telcoommmications
Reviewed on OpenReview:
Abstract
The ability to generalize to new environments is critical for deep neural networks. Most ex- isting works presume that the training and lest data share identical label set, overlooking the potential presence of new classes in test data. In this paper, we tackle a practical and challenging problem: Open-Set Model Adaptation (OSMA). OSMA aims to train a model on the source domain, which contains only known class data, and then adapt the trained model to the distributionn-shifeed target domain to classify known class data while identifying new class data, In this context, we face two challenges: (1) enabling the model to recognize new classes using only the known class data from the source domain during training, and (2) adapting the source- source-trained model to the target domain that contains new class data, To address these challenges, we propose novel and universal two-stage framework named Activate and Adapt (ADA). In the training stage, we extract potential new class informa- tion hidden within the rich semantics of the source domain data to enable the model to identify new class data. Additionally, to retain source domain information while preserving data privacy, we condense the source domain data into a small datasot, facilitating the sub- sequent adaptation phase. In the test stage, we adaptively adjust the source-trained model to the target domain with new classes by infusing the style of target data into the condensed datasct, and decoupling domain alignment for known and new classes. Experiments across three standard benchmarks dlmmnnstrae that ADA surpasses previous methods in both online and offline settings.
1 Introduction
Despite the remarkable success of machine learning in various fields, generalizing to new environments remains a significant challenge (Wang et al., 2022a). For instance, model trained on urban environment data to identify cars may perform sub-optimally problem. Recently, considerable efforts have beon dedicated to Domain Generalization (DG) (Zhou et al., 2022), which aims lo enhance model performance on previously unseen domains.
the rural environment due to the distribution shift
Current approaches within the realm of DG include learning representations that are invariant to domain changes (Lv et al., 2022; Lu et al., 2022; Chen et al., 2023d; Wong et al., 2024), expanding data distributions through data augmentation and synthesis (Huang et al., 2021; Su el al., 2023; Bose et al., 2023; Zhu et al., 2023), and employing meta-learning to expose models to various shifts (Wang et al., 2023b; Chen et al., 2023c), among others. Despite these advances, most methods operate under the assumption that the source and target domains share an identical label space, which limits their applicability to the closed-set scenario.
Email to: xwangfy@commett.us
Corresponding author

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A more practical and realistic concern is the existence of broader label space in the target domain. For instance, a model trained with previous DG methods may mitigate the effects of distribution shift between urban and rural environments. However, when the model encounters objects specific to rural areas, such as livestock, it may struggle to recognize them due to their absonce in the training data. This shortcoming could pallntially lead to significant safety risks, compromising the reliability and safety of machine learning systems in diverse environments.
To enhance the adaptability of DNNs in an open-world context, we investigate more practical and chal- lenging problem: Open-Set Model Adaptation (OSMA), The goal of OSMA is to train model on source domain data containing only known class data, and then adapt the model to perform effectively in the tar- get domain, which is characterized by distribution shift and open class challenges. Specifically, the model needs to classify known class data while also identifying new class data in the target domain. In particular, during the adaptation phase, while direct access to original source data is restricted, is allowed to utilize information extracted from the source data in a controlled manner (in our work, we condense the source data into small dataset). We provide comparison of OSMA with other settings in Table 1.
The critical challenges of OSMA lie in two aspects. Firstly, we need to equip the model with the ability to identify new (unknown) class data during the training stage, using only known class data from the source domain. Secondly, we need to adapt. the source-trained model to the target domain for safe model deployment, ensuring its capability of classifying known class data while also identifying unknown class data.
To this end, we propose a novel and universal two-stage framework named Activate and Adapt (ADA), which facilitates model adaptation under the dual challenges of distribution shift. and open class. Our approach comprisos three key components. Firstly, to address the first challenge, propose Unknown Activation, which extracts the unknown class information hidden within the rich semantics of the known class data from source domain. This module enables the model to identify unknown class data during the training stage. Secondly, to harness the source domain data information during the adaptation phase while safeguarding data privacy, we introduce the Source Condensation module to condense the source data into a small dataset. Thirdly, we propose the Open Model Adaplation stage, which adapts the source-trained model on the target domain at test time. This is achieved by injecting style information from the target domain data into the condensed data to provide supervisory guidance, and inducing a decoupled domain-aligned mejustment for the cross-domain recognition of both known and unknown classes.
The contributions of our work are summarized as follows:
We explore the Open-Set Model Adaptation problem and propose a two-stage framework, Activate and Adapt, to lackle the distribution shift and open class challenges in test environments.
We leverage new class information hidden within the rich semantics of known class data enable the model to identify new class data. Additionally, introduce innovative use of dataset condensation to preserve source domain information, facilitating the adaptation phase.
Extensive experiments on three widely used standard benchmarks demonstrate that ADA outperforms existing methods in both online and offline settings.
2 Related Work
Domain Generalization (DG). DG aims at enhancing model performance on unseen target domains. Existing DG methods can be categorized into different categories, such as data augmentation and genera- tion (Huang et al., 2021; Su et al., 2023; Bose et al., 2023; Zhu et al,, 2023), representation learning (Wang et al., 2022c; Lu et al., 2022; Wong et al., 2024), and meta-learning (Wang et al., 2023b; Chen et al., 2023c). Some works have considered the more realistic open-set DG (OSDG) scenario, where unknown classes appear in target domain. For example, CODA (Chen et al., 2023a) generates virtual unknown samples via manifold mixup (Verma et al., 2019). OneRing (Yang et al., 2022c) argues that any category not matching the ground truth should be regarded as the unknown class. However, the "adaptivity gap" (Dubey et al., 2021) persists when the source-trained model is directly deployed on the target domain without further adaptation.
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Table 1: Comparison different problem settings. 3²) is labeled source domain data and x' unlabeled target domain data. OSMA gets extracted source domain data information (ă, .) during training and utilizes it in the test stage. "Fine-tune" means updating the pource-trained model parameters in the test phase; "Online" means the model adapts and predicts after single pass over cach data while "Offline" means the model adapts on the full lest set before making predictions.
Training
Test
Problem Settings
Training Data
Training Loss
Fine-tune Adapt Loss Open-set Online
Offline
Open-Set DA Couree-Free DA
V.
Oix", .v0) fix", x0) ((x²)
x
X
X
v
V
Open-Set DG
C(x" .4¹)
X
X
Test-Time Adaptation
x², 15"
C(x",y)
D(x²) L( ,0,
X
V
x
OSMA
x², x'.8"
[(x"; :²)
V
v
Domain Adaptation (DA). The key difference between DG and DA is that DA has access to unlabeled target data for training while DG does not. Source free domain adaptation (SFDA) (Xia et al., 2021; Liang et al., 2020; Yang et al., 2022b; Wang et al., 2024) has been proposed to address the issue of source data unavailability when deploying the source-trained model to the target domain. For instance, SHOT (Liang et al., 2020) aligns cross-domain features using self-supervised pseudo labeling, and AaD (Yang et al., 2022b) employs prediction consistency and inconsistency refine feature representations. Some works further study the label-set shift setting. For example, GLC (Qu al., 2023) adopts clustering and pseudo-labelling tech- niques to identify known and unknown class data under category-shifts. LEAD (Qu et al., 2024) decouples features to identify target-private data. Inheritable model (Kundu et al., 2020) proposes the vendor-client paradigm to solve the open-set problem.
Test Time Adaptation (TTA). TTA is an emerging paradigm that enhances model generalizability through unsupervised fine-tuning in an online manner (Wang et al., 2020; Sun et al., 2020; Chen et al., 2022; Gan et al., 2023; Wang et al., 2023a; Chakrabarty et al., 2023; Wu et al., 2024: Su al., 2024). For example, Tent (Wang al., 2020) proposes entropy minimization to improve model confidence. TTT (Sun et al., 2020) introduces auxiliary self-supervised tasks to finc-tune the model. A few works explore the open-set setting in TTA. For instance, ART (Chen et al., 2023b) uses cross-domain nearest neighbor and class protolype information to detect open- apen-class data, and OSTTA (Lee et al., 2023) selects samples with higher confidence values to conduct entropy minimization.
Comparison of Different Settings. The comparison of OSMA and other settings shown in Table 1. OSMA differs from OSDA and SFDA in that it does not access target domain data during training. Compared to OSDG, which does not adaptively adjust the source-trained model at test time, and TTA, which adapts the model to the target data only in an online manner, OSMA leverages extracted source domain information to adapt the source-trained model on target data, and it is applicable in both online and offline settings.
3 Method
Design Overview. Our proposed Activate and Adapt (ADA) consists of two stages. In the first stage, Unknown Aclivation and Source Condensation (UASC), we equip the model with the ability to identify unknown class data using only known class data from the source domain for training. We also condense the source data to small dataset to preserve information. In the second stage, Open Model Adaptation (OMA), we utilize the condensed datasel along with larget domain data to jointly adapt the model and make final predictions for the target domain data. The illustration of ADA is presented in Figure 1.
Problem Sctup. We define the OSMA problem formally. In the training stage, we have N₂ labeled data from source domain D. {(a;, uf V Our lask is to classify N, unlabeled data from target domain D. [a We denote the label sets of D, and D. as C, and C, respectively. The target domain encompasses a wider class scope than the source domain, i.c., C, C Cr, C. (C, Cr. We refer C, as known classes and Cn unknown classes. In our setting, we take the unknown classes whole. Our goal is to
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Labeled Source Data
Reature Extractor
Feature Map
Grandlar Features
UASC
Unknown Activation
unkuoes gaodda feature of source data
kmown gramilrr feature source data
Source Condensation
Condensed Data
Style
OMA
Style-Injected CE
unkerow and knewan gromdrrr foceuuee condersedd disla
Unlabeled Target Data
unienowm and Tenown grandlar fealutes of target data
Unknown Class Alignment
Known Class Alignment
Figure 1: Illustration of ADA. ADA comprises two stages: Unknown Activation Source Condensation (UASC) and Open Model Adaptation (OMA). In UASC, we decompose the feature maps of source domain data to granular features, and activate the classification logit for the unknown class dimension using the classified unknown class granular features. We additionally condense a small dataset to preserve source domain information. In OMA, we inject the target domain style information to the condensed data for supervisory guidance, and align cross-domain knowledge for both known and unknown classes.
train model D, and then adapt the model on D, to classify the data in D, to |C,I+ classes, where the (Ic.) + 1)-th dimension means the unknown class. Note that the original source domain data D, is no longer available in the adaptation phase, while a lightweight proxy of the source data is allowed to be used.
3.1 Unknown Activation and Source Condensation
3.1.1 Unknown Activation
Unknown Extraction. DNNs trained with standard cross-entropy loss on known class data fail to identify unknown classes (Chen et al., 2023b). However, is overlooked that images contain rich semantics (Zhou et al., 2016; Li et al., 2023b) which can be leveraged as potential unknown class information. For example, the foreground of image showing bedroom may align with its ground-truth label "bed". However, there are additional objects in the background, such as "curtain" and "lamp", which might not be included in the source domain's 's label set. This obscrvation motivates us to extract unknown class information inherent in the rich semantics of the source domain data to activate the model's ability to identify unknown class data.
Specifically, we decompose the penultimate-laye image features into fine ame-grained features and then identify blocks with intrinsic unknown class information. Formally, we denote the feature extractor as ʃ and the classification head as g. For source domain data (z" y²), z" = f(x") E HC is the feature map extracted by f without the last global average pooling layer. We further decompose feature map z into H W fine- grained granular features [ .Z E Rº, representing the features extracted from different regions of the image. Then, we forward these granular features to the classification head independently to obtain their predictions argmax, 9e (Z,),, where g(.) E IR Ca/+1 denotes the output logit and g-(-) is the e-th element of g()). Based on whether thoir predictions align with the ground truth labels y", we categorize these granular features into known class feature group 2 and unknown class feature group 2 In formula,
2 = ( [ argmax, ge(Z,) = y²), - = (Z argmax, (z,) # )²}.
(1)
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prob upper image prob of lower image
upper imago
prob upper image
of image
lower image
Prahabiliyy
0.6
Prohabilig
0.6
Probstallt
0.6
Elephant
0.4
0.4
0.4
0.2
0.2
0.0
Elephant
Dog
Gimiloo
Guitar Unknown
0.0
Elephant
Dog
GimiTe
Guitr Unknown
0.0
Elephani
Dog
Girafle
Guster
Unknown
Unknown
(a) Train with
(b) Train with + LUE
(c) Train with LUE + Lui
Figure 2: Output probability distributions for input images processed by models trained with different losses. The blue bars represent the probabilities for the upper image from a known class "elephant", The grey bars indicate the probabilities of the lower image, belonging a category not included the training data, and thus should be classified the "unknown" class.
The unknown class features 2 are then used to activate the classification logit for the unknown class dimension (CC.) + 1)-th as follows:
111 (1 Enec,,111 exp(g.(Z)) (Z,))
LUE(E, v.)
log
(2)
2g
27E25
In other words, we assign the pscudo label (IC,) + 1) to these selected gramular features, treating them as unknown class data to explicitly activate the logit for the unknown class dimension. As shown in Figure 2(b), when the model trained with &CE+LUE. the probability for the unknown class is no longer zero. contrast, when trained with Lee (Figure 2(a)), the unknown class probability always zero for arbitrary inputs.
Unknown Improvement. To further increase the probability of the unknown class withoul impeding the known class classification performance, we introduce an unknown improvement loss related to general image classification to enhance the model's response to the unknown class dimension. To differentiate from z", we use AG RC to represent the feature extracted by f with the last global average pooling layer. formula, the unknown improvement loss is:
exp(g)..11((EAA))
LuI(EE vs) LCE(#s. Vn) log Σ Eee|c, exp(ge(Z'Ap)) Allg(2z
(2GAP))
In this loss, LCE = - log Eeg(c,+1 ( is the standard cross-entropy loss, which leads the training
process and guarantees the known class performance. The sccond term is designed to enhance the model's classification logit for the unknown class dimension (IC,) + 1)-th for any input z", irrespective of its ground- truth label. Note that in the denominator, we exclude the term of the ground truth label y", thereby preventing degradation of the classification performance for known classes. The last L2 regularization term serves increase the smoothness of the classifier's output.
Final Objective for Training. Hence, final objective in the training stage is
LUR(x,, Ws) + Lur(Ha,
As shown in Figure 2(c), when the model is trained with the full loss LOe + LUI, the real unknown class image successfully identified (grey bars) since the classification logit of the unknown class dimension is activated by Lug and further enhanced by LUI. Moreover, the known class sample is also classified correctly (blue bars) even though the probability for unknown class dimension is uplifted.
3.1.2 Source Condensation
Due to safety and data privacy concerns, SFDA and TTA methods arise adapt trained model to the target domain without accessing source training data. However, discarding source data entirely during
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adaptation restrict potential performance gains, as it eliminates the only source of precise supervision. This motivates us to leverage source data while ensuring privacy. We achieve this by condensing the source data into small dataset that retains class-wise information.
Following recent studies (Wang et al., 2022b; Zhao & Bilen, 2023; Kang et al., 2023), we optimize the condensed dataset Deo = Ueec, {( e by matching the feature distributions of condensed data and source domain data for each known class C.. More specifically, we utilize the source-trained feature extractor f and freeze it the backbone for our Source Condensation. For each class c, we sample batch of synthesized samples Be = (( and real source domain samples B {( e)],e and minimize the empirical estimate of maximum mean discrepancy between their features extracted by f. Morcover, it is known that visual domain has been characterized by image styles, which are commonly described by low-level features' statistics, and instance normalization (IN) normalizes image styles (Ulyanov et al., 2017; Huang & Belongie, 2017; Nam Kim, 2018). Hence, obtaín less donain-specific condensed dataset, we apply IN to the low-level features of the source domain data B. We denote f fi f2, where fi and f2 are the upper and lower parts of (detailed in Appendix A). The objective is:
Ic,1
DDo(Dcon: Dz) =
Σ
fo Bel
Σ x'ER
(5)
BY
ReB.
This process yields small condensed dataset Dom that retains class-wise information while removing source domain image styles, facilitating the subsequent model adaptation stage.
3.2 Open Model Adaptation
Although tackle the open class problem in the UASC stage, deploying the source-trained model in open and new environments remains unsafe and unreliable. We identify two key challenges: (1) semantic misalignment arising from the differing distributions of source and target domains; and (2) asymmetric transfer caused by the mismatched label sets of source and target domains. To tackle these issues, we propose the Open Model Adaplation (OMA) stage, which includes two adjustment. terms facilitate reliable model adaptation in the open-set target domain.
3.2.1 Supervisory Guidance with Style Injection
In Source Condensation, we obtain a condensed dataset that normalizes image styles. However, the class- wise content information, which transferable across domains, preserved. When combined with the style of target domain data, the condensed dataset can serve as synthetic target samples. This motivates us inject the style information of the target data into the condensed datasct to provide supervisory guidance for known class adaplation. Denote 2N f2(ā) and zı f2(x²) the low-layer features of condensed data (ū, ū) and target data x' respectively. We conduct style injection (SI) by injecting low-layer feature statistics of the target data into the condensed data:
21 p(Z))
SI(ž,) u(zi) + ozzi)
(6)
o(2))
where ux() and a--) are the channel- mean and standard deviation of the low-layer features, respectively. In formula, they are calculated in terms of feature map z E H'' xW" as follows:
H W
m(2)
HWW
(7)
H W
1
o(z)
(z, 11(2))²
H'W
w=1

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Then, utilize the style-injected feature žsι fi(((())) of the condensed data to provide supervisory guidance for model adaptation:
(si(2.)) 9) = - log exp(gy(Zst))
(8)
Eee C,/+1 (Zs))
where ij is the supervisory label of condensed data T. Intuitively, style-injected condensed data exhibit target domain style and known class content, making them suitable for providing supervisory guidance for known class adaptation. In other words, we treat the target-style injected condensed data as virtual known class data from the target domain, and adapt the model to recognize them during the test phase.
3.2.2 Decoupled Domain Alignment
Asymmetric label sets of source and target domains cause mismatches for known and unknown classes. We propose to align the cross-domain knowledge for both known and unknown classes. The core idea is to adapt the model to insensitive to domain variations when recognizing known and unknown classes
In the OMA stage, have access to the condensed dataset Doon and unlabeled target domain data Dr. We decompose the feature maps (extracted by feature extractor without the global average pooling layer) of condensed data 2 f() E RC*HXW and target data z' = f(x') E RC into granular features ) E RC and / N=WW E RR, respectively. For each granular feature Z, we forward it to the classifier to obtain its probabilitios for being known and unknown classes as follows:
(Z) Eeeic,1 (x)(
Eeelc,1t1 exp(ge (Z))*
(9)
exp(9|c,1+1() (Z))
(Z) Eeeeci+1 exp (Z))
Then, introduce two lightweight binary domain classifiers Is and 9u. which are designed to discern whether the granular features (classified as known and unknown classes) are from the condensed data target data respectively. We achieve cross-domain knowledge alignment by raising the model's insensitivity of domain discrepancies. More specifically, we denote D = {0, 1} as the domain label, referring that gramular feature Z is from the condensed data (label 0) or target data (label 1). Let Po. (Z) denote the probability that the known-class domain classifier 9s classifies the gramular feature Z as belonging to the condensed dataset (domain label 0), and PI,N (Z) denote the probability that Z belongs to the target domain (domain label 1). Similarly, PZ..()) and Pı,, (Z) are the corresponding probabilities obtained after forwarding the unknown-class domain classifier Ja. The domain alignment loss is weighted binary cross-entropy loss:
1 (DA( HW
Σ
(Z))D)ooggP.... (Z)) + (1 D) Dog(,,,
ZE(Z,
(10)
We (Z) [og(oopp..u (Z)) + (1- log(pI.,
For granular features from the condensed data Z E [ N= (domain label D = 0), LDA raises the probabilitios (Z) and (i,,(() for being classified as from the target data. Similarly, for granular features from the target data E ) NN#H&W (domain label D 1), LDA increases their probabilities (Z) and (Z)) for being classified as from the condensed data. In this way, raise the model's insensitivity to the domain variances, enforcing the model to align cross-domain knowledge for both known and unknown classes.
Final Objective for Adaptation. Hence, the final adaptation loss for our OMA stage is:
LMA (ă, x.) = Lo xx) + aLsu(ū, Ø).
(11)
where a a balancing syper-parameten Intuitively, Esi enhances the performance for known class classi- fication performance, while LDA facilitates the model identify unknown class data in the open-set target domain. These two terms work collaboratively update the feature extractor f, main classifier g, along with two lightweight binary domain classifiers Pk and Iu during the OMA stage.
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4 Experiments
4.1 Experimental Setup
Benchmarks. We conduct extensive experiments three standard DG benchmarks to validate the effec- tiveness of our proposed ADA: (1) OFFICE-HOME (Venkateswara et al., 2017) contains images from 65 classes across four domains (Artistic, Clipart, Product, and Real World). The first 15 alphabetically ordered classes are designated as known classes C, and the subsequent 50 classes are treated as the unknow class CP, (2) OFFICE-31 (Saenko al., 2010) comprises 31 classes of images sourced from three domains (Amazon, DSLR, and Webcam). The ten classes shared by OFFICE-31 and CALTECH-22 (Gong et al., 2012) are adopted as C, and the alphabetically last 11 classes are C.¹ (3) PACS (Li et al., 2017) consists of images from four domains (Photo, Art Painting, Cartoon, and Sketch) with distinct styles. It has seven classes in total. Four classes (dog, elephant, giraffe, and guitar) constitute Cs, and other classes are Cy
Evaluation Protocols. Following prior works (Bueci et al., 2020; Chen et al., 2023a), we adopt the h-score (hs) as the key evaluation metric, which emphasizes importance for both known class accuracy ace; and unknown class accuracy 6CCa. More specifically, hs calculated as 2 acea Xeec, and a high hs requires both accs and acc, to be high and balanced.
acc2+acc,
Implementation Details. For all experiments, we use the ImageNet pre-trained ResNet-50 (He et al., 2016) as the feature extractor and two fully connected layers as the classification head, which is consistent with previous works ( (Yang et al., 2022b; Liang et al., 2020) for a fair comparison. The binary domain classification head in the OMA stage consists of two we use SGD with a momentum of 0.9 as the optimizer, and the batch size is set as 64. The learning rate is set as 1o-3 for the backbonn and 1c-2 for the classifier, and the training epoch is set to 30 for all datasets. In the OMA stage, the fine-tune learning rale is reduced to one-tenth of the learning rale adopted in the training stage. More details are provided in Appendix A.
-N-aa--B-Laa blocks and a linear layer. During training,
4.2 Baselines
We conduct experiments in both online and offline settings. For the online setting, we compare ADA with TTA methods, including Tent (Wang el. al., 2020), OSTTA (Lee el al., 2023), UniEnt (Gao et al., 2024), and ART (Chen al., 2023b). Note that except for Tent, others are specifically developed for the open- set setting. For the offline setting, et al., 2020), AaD (Yang et al., 2022b), and OneRing (Yang et al., 2022c). Some methods such as Tent are incapable of directly handling the open-set scenario, and we adapt these methods for the open-set scenario by calculating their predicted probabilities' entropy and predefining an entropy threshold (Zhu & Li, 2022). Specifically, we set the threshold as 0.5 log [C,), and the data is classified as unknown class when the entropy of the probability distribution is larger than predefined threshold. Details are provided in Appendix A.
compare with SFDA and OSDG basclines, including SHOT (Liang
4.3 Main Results
We compare our method with baselines in both offline and online settings. The detailed results are reported in Table 2 (OFFICI-HOME and PACS) and Table 3 (OFFICE-31). We elaborate our findings as follows.
Offline Setting, In the offline setting, the model makes predictions after being fine-tuned on the entire unlabeled target dataset. The results indicate that ADA exhibits a significant performance advantage over baseline methods across all three benchmarks. For instance, ADA outperforms the best baseline method in hs by 4.9% for OFFICE- -OFICE-HoMe, 7.3% for PACS, and 4.6% for OFFICE-31. More specifically, we point out two key findings: (1) ADA consistently delivers high hs across individual tasks by effectively balancing both acc; and acc.. In contrast, other methods such as SHOT often yield imbalanced results on some adaptation tasks, thus leading to inferior hs. This is likely due to their reliance on the additional step of predefining a threshold for distinguishing the unknown class. The optimal threshold can vary significantly across individual tasks, making it difficult to set a consistently optimal value. ADA introduces the additional unknown class dimension (C.I + 1)-th to the classifier, thereby avoiding this issue. across different benchmarks. For instance, SHOT only brings marginal improvement compared to FRM on
(2) The performance of baseline methods varies
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Table 2: Results of all experiments for OPFICE-HOME and PACS. We highlight the best and second results.
OFFICE-
A2C
A2P
A2R
C2A
C2P
C2R.
Setting
Methods
aC0;
hs
RO0K
h8
a02;
a00g
000%
(00%
00Ca
h8
a0Ck
000g
Tent
61.7
50.3
55.4
77.5
48.1
59.4
88.5
45.3
59.9
67.9
62.8
59.4 4
75.7
42.2
64.2
82.5
43.4
56.8
OSTTA
52.7
65.0
58.2
63.5
63.4
63.5
72.3
55.5
62.8
55.2
67.4
60.7
63.2
69.9
66.4
72.2
71.9
72.0
Online
UniEnt
48.6
54.9
52.5
62.9
89.0
45.2
68.4
51.1
76.5
53.2
57.0 54.8
55.9
78.8
52.1
62.8
87.5
61.6
72.3
63.4
65.6
64.5
76.4
50.4
60.8
81.1
63.0
70.9
ADA
52.2
80.7
63.4
71.4
74.3
72.8
84.4
73.9
78.8
42.9
87.2
57.6
58.4
76.7
66.3
65.4
79.6
71.8
ERM
35.5 76.1
48.4
48.5
62.3
54.5
61.7
51.2
56.0
42.1
64.7
51.0
52.0
47.1
49.4
61.8
52.1
56.5
SHOT
71.7 31.6
43.9
26.6
41.0
91.8
34.6
74.2
50.7
82.2
27.2
40.8
86.5
34.8
Offline
AaD
51.2 61.3
55.8
56.7
61.3
82.6
63.88
72.0
60.0
77.1
67.8
60.7
64.0
76.4
65.9
OneRing
50.7 76.4
61.0
75.3
64.8
69.7
86.9
64.8
74.2
55.0
77.0
64.2
64.8
67.7
66.2
72.3
60.8
66.1
ADA
55.8
80.1
65.8
72.1
75.3
73.3
84.0
74.8
79.1
45.9
83.1
59.1
62.0
73.2
67.1
70.6
75.5
73.0
P2A a00k
P2C
P2R
R2A
R2C
R2P
Average acc.
Methods
acc,.
h8
o.cp
a0cy
ha
ROCk
OCC,
hs
(.0%%
O0C
ha
000k
0CC,
000%
DCC,
ha
aCOk
hs
Tent
62.9 60.6 61.7
58.8
53.6
56.1
85.7
47.6
61.2
75.1
52.7
62.0
63.1
51.9
56.9
85.8
46.6
60.4
73.8
49.6
58.6
OSTTA
47.2
81.8 59.9
47.4
75.6
58.3
75.7
61.2
67.7
64.1
72.9
68.2
53.9
74.6
62.5
80.1
70.8
75.2
62.3
69.2
64.6
UniEnt
63.7
58.7
61.1
60.0
50.0
54.6
84.6
56.4
67.6
77.4
49.4
60.3
64.3
49.1
55.6
84.0
57.7
68.4
74.1
50.4
59.6
ART
63.5
48.4
54.9
55.5
54.3
54.9
87.5
52.2
65.4
59.2
52.8
55.8
87.7
54.2
67.0
74.6
57.0
64.6
63.2
57.2
62.5
ADA
42.9
83.5
56.7
79.9
56.0
76.5
77.8
77.1
59.5
79.0
49.0
78.5
79.5
75.2
77.3
60.4
78.9
67.2
ERM
39.3
68.6
50.0
32.2
44.3
37.3
62.1
65.0
63.5
45.1
44.9
45.0
44.6
36.2
40.0
76.2
37.0
49.8
50.0
54.1
50.1
SHOT
72.1
54.9
62.4
64.7
30.8
41.7
89.6
39.0
54.3
77.1
52.4
62.4
64.5
37.1
47.1
86.9
34.6
49.4
79.2
37.9
50.3
AaD
34.9
80.5
65.3
50.4
63.9
56.4
77.6
68.4
72.7
61.8
74.3
67.5
54.9
59.7
57.2
76.6
59.1
66.7
65.1
65.9
64.8
OueRing
62.8
61.2
51.1
68.9
69.2
70.8
65.8
49.0
70.8
85.0
61.7
71.5
ADA
44.6 79.5
57.1
49.5
75.4
59.7
76.5
77.7
77.1
63.1
77.3
69.5
53.1
73.7
61.7
79.5
75.2
77.3
63.1
76.7
68.4
A2C
A2P
A2S
C2A
C2P
C2S
Setting
Methods
00%
hs
800%
OCG,
hs8
40%
400
000g
000
400%
000,
hss
000k
00G,
Tent
53.1
43.6
47.9
40.0
43.6
41.7
64.7
40.6
49.9
74.3
30.1
42.8
75.7
28.6
41.6
69.5
32.8
44.5
OSTTA
54.2
55.5
54.8
44.2
43.6
43.9
43.1
52.4
47.3
62.2
46.1
52.9
72.6
39.0
50.7
60.0
55.5
57.7
Online
UniEnt
55.5
41.5
47.5
42.2
47.2
44.6
67.1
43.0
52.4
64.8
30.5
41.5
89.6
28.1
42.8
70.1
32.9
44.7
ART
52.3
70.2
50.9
27.4
39.3
32.3
50.7
51.4
51.1
57.3
63.8
60.4
87.0
67.6
76.1
60.6
73.9
66.6
ADA
64.1
62.0 60.0
29.9
53.0
38.3
28.9
73.3
41.5
95.7
69.5
80.5
59.2
50.6
54.6
55.0
51.6
53.3
ERM
38.4
80.0
51.9
90.0
92.6
42.3
54.0
47.3
36.6
75.9
49.4
64.5
86.3
73.8
48.7
SHOT
82.7
32.7
46.9
99.0
32.6
49.1
76.4
32.8
45.9
S0.8
34.2
96.7
45.8
62.1
61.6
31.3
Offline
AaD
74.9
44.4
55.8
78.3
52.8
63.1
54.5
30.5
39.1
40.8
42.2
41.5
56.0
35.2
43.3
31.4
26.4
28.7
OneRing
65.4
38.0
48.1
98.7
55.4
71.0
53.4
38.7
44.9
54.2
47.6
50.7
73.2
41.8
53.2
60.3
55.6
57.8
ADA
58.8
55.3
57.0
40.6
75.3
52.8
19.2
82.7
31.2
95.4
88.0
91.6
64.0
61.9
62.9
62.4
57.6
59.9
P2A a00g
P2C
P2S
S2A
S2C
S2I
Average acc,
Methods
occ,
h8
0.0g
400g
hs
0.0%
0.0
hs
(L0C%
00C,
hi
0.0g
0.0,,
000%
000,
ha
000k
hs
Tent
77.1
58.1
66.3
50.3
66.3
57.2
42.0
64.5
50.9
67.8
49.5
57.2
69.9
38.6
09.8
55.0
55.0
55.0
61.6
45.9
50.4
OSTTA
68.3
69.5
68.9
47.0
34.4
39.7
33.7
72.8
46.1
68.3
71.5
69.9
53.1
65.3
58.6
65.0
33.9
44.5
56.0
53.3
52.9
Uni
77.7
57.8
66.3
57.1
64.5
60.6
44.6
61.2
51.6
78.3
49.4
60.6
72.4
38.8
50.6
91.4
55.4
69.0
67.6
45.9
52.7
ART
70.0
79.2
74.3
61.1
33.9
57.5
39.3
46.3
18.6
42.1
89.0
75.7
81.8
ADA
71.3
72.7
72.0
51.1
54.7
52.9
51.2
68.7
58.7
25.7
86.9
39.7
17.4
89.4
29.1
59.6
62.1
60.8
66.0
53.6
ERM
43.2
95.2
59.5
12.5
82.4
21.7
14.7
99.5
25.6
16.7
44.3
24.2
21.2
41.0
28.0
24.8
29.6
27.0
36.9
72.6
45.8
SHOT
80.1
37.5
51.1
74.7
31.6
44.4
73.5
20.3
31.8
47.8
32.9
39.0
71.9
39.3
50.8
95.2
41.0
57.3
78.4
34.3
47.3
AaD
60.0
53.4
47.7
54.3
66.6
54.9
60.2
73.1
70.6
71.0
58.9
64.4
85.5
48.5
54.4
OueRing
71.5
67.6
32.1
57.9
41.3
30.5
50.1
40.2
39.1
86.3
53.8
54.0
62.9
58.1
56.0
90.3
69.1
57.4
54.7
ADA
68.4 82.4
74.8
49.7
67.6
57.3
48.9
75.8
59.4
37.3
76.7
50.2
32.5
83.0
46.7
69.6
54.2
60.9
53.9
71.7
58.7
OFFICE-HOME and PACS. In contrast, ADA consistently delivers enhanced results on all three benchmarks. highlighting the significance our designed two stages.
Online Setting. In online setting, target data continuously arrives, and the model adapts on it and makes prediction presently. We compare ADA with TTA baselines, and it shows that ADA surpasses baselines three

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Published in Transactions on Machine Learning Research (07/2025)
Table 3: Results of all experiments for OFFICE-31, We highlight the best and second results.
OFFICE-31
A2D
A2W
D2A
D2W
W2A
W2D
Average
Methods
000k
000
(60%)
000,
000%
000g
40%
000
h8
000K
000
h8
600g
00%
Ius
000
000
Tent
82.6 54.6
65.8
86.9
67.7
69.3
61.4
89.0
72.6
88.3
81.7
84.9
79.4
75.7
77.5
95.0
77.7
85.5
82.2
72.7
75.9
OSTTA
81.2 83.0
82.1
75.1
80.2
77.5
34.8
98.4
51.4
83.8
96.2
89.6
63.2
92.5
75.1
97.2
93.6
95.4
72.6
90.6
78.5
UniEnt
90.3 60.6
72.5
86.9
57.3
69.1
64.3
87.0
73.9
98.3
81.7
89.2
80.6
74.9
77.7
98.2
77.1
86.4
86.4
73.1
78.1
ART
91.0 66.5
76.8
86.2
63.7
73.2
56.3
90.7
69.4
68.0
86.1
76.0
76.1
81.9
78.9
95.8
79.3
86.8
78.9
78.0
76.9
ADA (Online)
88.2
79.5
76.3
73.2
74.7
62.7
88.6
82.7
88.0
85.4
78.6
85.9
92.0
ERM
79.9
71.9
75.7
78.1
82.0
80.0
13.0
90.2
22.7
18.2
95.0
30.5
30.8
98.1
46.9
88.2
93.6
90.8
51.3
88.5
57.8
SHOT
90.5
38.7
54.2
91.4
51.2
65.6
83.7
33.3
47.7
96.3
78.6
86.6
77.0
44.6
56.5
93.0
82.8
87.6
88.7
54.8
66.3
AaD
75.0
75.3
75.2
78.5
74.6
76.5
72.6
67.9
70.2
90.2
86,4
88.3
70.8
72.4
71.6
94.3
91.3
92.8
80.2
78.0
79.1
QucRing
81.5
70.9
75.8
76.5
56.8
65.2
69.4
88.8
77.9
85.0
70.3
76.9
73.7
85.8
79.3
89.2
86.6
87.9
79.2
76.5
77.2
ADA (Offline)
88.2
80.3
84.0
75.4
73.6
74.5
75.5
82.0
78.7
95.6
80.8
87.6
74.5
83.4
78.7
97.9
87.8
92.6
84.5
81.3
82.7
benchmarks consistently. Compared to previous best methods, ADA improves the hs by 4.0% for OFFICE- HOME, 0.8% for PACS, and 4.1% for OFFICE-31. Furthermore, we observe slight decrease in ADA in the online setting compared to the performance in the offline setting. This can be attributed to the "one-pass" nature of the online adaptation setting. Nevertheless, compared to existing open-set TTA methods such as OSTTA, UniEnt, and ART, ADA demonstrates improvements across all benchmarks. These results suggest that ADA is effective for both online and offline scttings.
4.4 Ablation and More Studies
A Ablation on Two Stages. Table 4 shows the ablation results of the proposed two stages UASC and OMA. It can be observed that the combination of the two stages outperforms all other variants. Moreover, we find that even with the UASC training stage alone, our method achieves competitive results. This suggests that UASC is effective in enabling the model to identify unknown class data. To further examine the efficacy of the OMA stage, we integrate UASC with existing SFDA and TTA methods that address the domain shift problem, including SHOT (Liang et al., 2020), Tent (Wang et al., 2020), TTT (Sun et al., 2020), and UniEnt (Gao et al., 2024). As shown in the table, UASC exhibits worse performance when combined with SHOT, TTT, and Tent, and a marginal improvement observed when combined with UniEnt. These findings substantiate that collaboration of the UASC and OMA stages maximizes the effectiveness of ADA.
Table Ablation results (hs, %) on three benchmarks.
Methods
OFFICE- HOME
OFFICR-31
PACS
Average
LUE
65.1
72.3
48.6
62.0
Lui
63.8
64.5
48.0
58.8
LUB + LUI (UASC)
66.0
80.1
51.8
66.0
UASC+LDA
65.5
80.3
53.6
66.5
UASC EsI
66.1
81.8
53.1
67.0
UASC SHOT
61.4
74.5
46.4
60.8
UASC Tent
63.1
77.8
53.6
64.8
UASC+TTT
62.8
80.4
47.9
63.7
UASC Unnint
66.2
80.3
53.4
66.6
UASC+LDA CCi (UASC+OMA)
68.4
82.7
58.7
69.9
Analysis on Unknown Activation. In the UASC stage, we design Lue and LUi to activate and enhance the model's response for the unknown class. The results of their individual ellects are shown in Table 4. It shows that when these two losses are combined, the model exhibits better performance than their individuals. We present an illustration in Figure 2, showing the probability distributions when inputs pass models trained with different losses. It shows that when the model is trained with the full loss Lue + LUI. effectively distinguishes unknown class data while maintaining its performance for classifying known class data.
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Published in Transactions on Machine Learning Research (07/202) 2025)
bocore (%)
90
90
metrics (%)
metrics (%)
80
30
70-
4.2
0.1
50
0.00
0.05
0.10
0.15
0.20
50
0.00
0.05
0.10
0.15
0.20
butch size
0.65
a
(a) a in online setting.
(b) a in offline setting.
(c) Batch size sensitivity.
Figure 3: (a)- Sensitivity of for online and offline settings respectively. (c) Sensitivity of test batch size for online setting. All results are OFFICE-HOME.
Analysis on Open Model Adaptation. The ablation results of two losses LDA and Lst proposed in OMA are provided in Table 4, and shows that each loss contributes marginally, while the joint of them achieves better results. Moreover, we study the sensitivity of the hyper-parameter a in Equation 11. In Figure 3(a) can observe that as increases, acc; increases while acc, decreases in both online and offline settings. This aligns with our intuition that Lsi enhances known class performance and LDA strengthens unknown class identification. When is around 0.05 to 0.1, a balance between ace; and acc, is achieved, resulting in the highest hs. Furthermore, our analysis reveals that the resulls are more stable to variations in in the online setting compared to the offline setting. This can be explained by the fact that in online adaptation, the model updates and predicts incrementally as it contimuously receives data from target domain. Hence, the model's adjustments are more gradual and controlled in the online setting than in the oflline setting.
We also investigate the influence of varying test batch sizes in the online adaptation context. The result is shown in Figure 3(c). It can be observed that hs is not significantly affected by the variation of batch size and a. This insensitivity underscores the consistent performance of ADA across different conditions, highlighting its robustness in a variety of scenarios.
Analysis on Source Condensation. We empirically study the impact of the condensed dataset. Our analysis focuses on two aspects. First, we assess the benefits of employing a condensed dataset over the raw training data. We explore two alternative strategies: randomly sampling subset from the source training data, and selecting samples that are closest to the class prototype for each class. As shown in Table 5, the strategy of employing condensed dalasel oulperforms the other two variants, indicating that the condensed dataset offers more comprehensive information than a selected subset of the source data, Notably, the performance of all three stratogics exceeds that of the scenario where the OMA stage is ablated (51.8% as shown in Table 4), underscoring the importance of the OMA stage. Moreover, using the original source data carries a risk of data privacy leakage, whereas source condensation avoids this issue.
Table 5: Impacts of condensed dataset and different sampling strategies. hs for PACS are reported.
Strategies
Random Selection Class Prototype Condensed Dataset
hs
56.9
56.7
58.7
Second, we investigate the impact of the number of condensed data. As shown in Figure 4(a), ADA remains effective when the number of condensed data per class is at least 10. This indicates a small condensed dataset can preserve enough source domain information, reducing the computation burden of the OMA stage.
Feature Visualization. We present the GradCAM (Selvaraju et al., 2017) visualization of ADA and ERM in Figure 4(b). Note that although ERM does not have supervision for the unknown class training. we heuristically visualize its (CC/+1)-tth output to explore its response to unknown-class inputs, which serves
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Published in Transactions on Machine Learning Research (07 (025)
Known Class
Unknown Class
I-score (%))
35-
50
20
40
100
number of samples
(a) Varying mumber of condensed samples.
GradCAM visualization.
Figure 4: (a) Impact of varying number of condensed images per class of PACS. (b) GradCAM visualization of ADA (top) and ERM (bottom) on PACS.
exploratory illustration. It shows that ADA demonstrates tendency to focus semantically meaningful regions. The activation maps generated by ADA are more comprehensive and precise, highlighting its ability to localize and understand objects in the image.
Figure shows the UMAP (MeInnes et al., 2018) visualization of features extracted by models trained with different methods: ERM, UASC (the first stage of ADA), and the complete ADA. Tt reveals that ERM is deficient in discerning known and unseen classes, resulting overlapping boundarics. UASC improves this separation, and the full ADA yields the most distinct class boundaries.
backpack
bike
calculator
headphones
keyboard
laplop
monitor
mug
pen
(a) ERM
UASC
ADA
Figure 5: UMAP feature visualization of different methods. Distinct colors refer to different categorics while grey dots represent the unknown class data in the target domain.
5 Conclusion
In this paper, we propose ADA tackle the OSMA problem, where both domain shift and open class challenges arisc in the target domain. In the training stage, uncover unknown class information hidden within the rich semantics of source domain known class images to enable the model to identify unknown class data. We additionally condense a small dataset to retain source domain data information, facilitating the subsequent adaptation process. During the test phase, we leverage the condensed data and target data to adapt the source-trained model to accommodate the open-set target domain. Empirically, ADA achieves superior performance on standard benchmarks both online and offline settings.
Acknowledgement
This work is supported by the Research Grants Council (RGC) Hong Kong, SAR, China and the NSFC/RGC Joint. Research Scheme Grant N HKUST635/20.
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Published in Transactions on Machine Learning Research (07 (225))
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A Method Details
A.1 Algorithm
Our proposed Activate and Adapt (ADA) consists of two stages: Unknown Activation and Source Conden- salion (UASC) and Open Model Adaplation (OMA). Two stages are summarized in Algorithm 1.
Algorithm Activate and Adapt (ADA)
Input: Source domain data D. { yt } feature extractor f, classifier 9, domain classifiers gk: 9u
Output: Label predictions for target domain data Dr {-
STAGE UASC
for source training epoch do Train f and g with LUA in Eq. 4
4: end for
5: Initialize condensed data Doon = [ e) for each class c E CN; freeze f
6: for condense training epoch do
7: Optimize Doon with Lsc Eq.
end for STAGE 2: OMA
10: for x' in D, do
11: Sample (ī, 5) from Doon, and get copy (7,
12: Forward x² and (ă, ÿ) to get low-level features 2₁ and ži; inject target data style to ž, by Eq. 6 and then calculate Lsi by Eq. 8
13:
Forward x' and (I', 7') to gel LDA by Eq.
14: Get total loss by Eq. and backward propagate update f, 9, Ik. and 9n
15: if Online then
16: Predict for x¹
17: end if
18: end for
19: if Offline then
20: Predict for all x' De
21: end if
A.2 Hyperparameters
UASC. We have detailed the source training parameters in the main paper. Here, provide the parameters for the Source Condensation part. Since we adopt the ResNet-50 as the backbone, apply the Instance Normalization to the output of the second residual block of ResNet-50. We adopt the SGD optimizer with a learning rale of 5.0, and we optimize the condensed images for 10,000 epochs. For each known class, we condense 10 images.
OMA. For the Style Injection operation, we apply it to the output of the second residual block of ResNet-50, aligning with the layer selected in the Instance Normalization in UASC.
A.3 Model Selection
In the training stage, we train the model for 30 epochs for all datasets. We select the final epoch model for the subsequent OMA stage. It is justified in domain generalization, where the access to train-validation split for model selection is typically unattainable.
A.4 Baselines
Some baseline methods such as Tent (Wang al., 2020) and ERM (Vapnik, 1999) are not specifically designed for handling the open-set classification scenario. We adapt these methods for the open-set classification
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setting by predefining entropy threshold (Zhu & Li, 2022). We set the threshold 0.5 log [Cal, where C.l is the number of known classes. Specifically, during the test phase, we calculate the entropy of the probability distribution for each data with respect to the [C,] dimensions. If the entropy is below the threshold, we predict as known-class data, and the predicted class is the known class with the highest predicted probability. the entropy is above the threshold, we predict as an unknown class data.
B
More Results
B.1 Additional Studies
We provide more results this section.
Impact of Test Order. In our online setting, the target data arrives continuously, and for each batch of target data, the model updates it and predicts presently. Here we investigate the order in which the target domain data arrives. For each order, we randomly shift the order of the whole target dataset. The result is present in Table 6. Our results indicate that the OMA phase exhibits robustness against variations in the order of larget data, thereby evidencing its adaptability and robustness in open-world context.
Table 6; Impacts of the order of target data in the online setting.
Order
OFFICE- HOME
OFFICE-31
PACS
Average
67.2
81.7
53.6
67.5
67.7
81.2
53.9
67.6
67.1
82.3
54.0
67.8
66.6
81.8
53.1
67.2
Impact of Number of Known Classes. We investigate the impact of the varying number of known classes three methods including SHOT (Liang al., 2020), AaD (Yang et al., 2022b), and our proposed method ADA. We keep the number of total classes (IC, + (C unchanged while changing the number of known class |C.| in the source domain. As shown in Figure 6, our method consistently outperforms SHOT and AaD across settings with different numbers of known classes. Notably, the advantage of ADA is pronounced in scenarios with very few known classes. This highlights its effectiveness situations where the supervisory information is scarce.
ADA (Curs)
AnD
80
SHOT
h-score (%))
60
40
20
5
10
15
20
25
number of classes
Figure 6: Impact of varying number of known classes for OFFICE-HOME.
Study of Unknown Improvement Loss Cui. We investigate the Cui (Eq. 3) in two aspects. Firstly, we study the impact of the L2 regularization term in Lus, which serves smooth the classifier's output, and subsequently to increase the probability of the unknown class. Table presents the results (hs after the first training stage) of using La2 regularization and label smoothing. It shows that La regularization performs
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better than label smoothing across three datasets, while both these two methods surpass the case where regularization term is included.
Table 7: hs (%) of different methods in three benchmarks.
Methods
OFFICE-HOME
OFFICE-31
PACS
N/A
63.4
77.1
50.4
label smoothing La regularization
64.1
78.3
50.8
66.0
80.1
51.8
Secondly, we study the impact of Lui on the known class performance acck- The unknown improvement loss Lui is designed to enhance the model's response to the unknown class in the absence of real unknown- class samples. As shown in the ablation results in Table 8, while Cui causes slight decrease in acck, it significantly improves acca; resulting in a higher hs.
Table 8: Results (average) of different training losses on PACS.
Losses
aCck
aCCa
hs
LUE 54.6
45.9
48.6
LUE + Lui
50.9
55.7
51.8
Results on Higher Resolution Backbone. We provide results of ADA and other methods on higher resolution backbone (ResNet-101 with 384 x 384 input sizc). Note that our method applics to CNN architcc- tures since relies on spatially local features (Section 3.1.1). Results in Table show that outperforms basclines with this larger backbone, and they also surpass those in our main results (ResNet-50).
Table 9: Results with ResNet-101 backbone.
OFFICE- HOME
OFFICE-31
PACS
Methods
acek
acCu
hs
acok
aCCC
Ius
aOCk
acCu
Its
Tent
77.1
50.9
60.2
85.4
61.0
69.1
41.4
53.3
45.9
OSTTA
63.2
70.1
65.4
79.2
79.2
78.8
57.2
50.4
53.5
UniEnt
70.2
62.4
66.3
79.2
82.0
80.3
67.9
47.7
53.3
ART
64.3
72.0
67.9
82.3
80.4
81.3
62.1
46.3
53.8
ADA (Online)
64.5
76.1
69.1
80.5
84.8
82.1
60.7
54.5
55.9
ERM
57.1
35.5
55.8
92.2
65.2
72.9
31.7
66.6
42.0
SHOT
72.1
55.4
60.0
78.6
55.7
65.8
68.9
42.3
49.1
AaD
67.8
72.7
68.1
79.7
80.4
80.5
67.8
53.0
57.5
OneRing
64.7
73.9
67.6
79.8
87.7
81.3
62.4
50.3
55.9
ADA (Offline)
66.3
76.6
70.4
81.5
85.4
83.0
61.9
55.3
59.2
Results on More Datasets. We additionally provide results on VisDA (Peng al., 2017). Six classes are designated as known classes C, (bicycle, bus, car, motorcycle, train, truck), and the remaining six classes are treated as the unknown class C,.. Results in Table 10 show that ADA consistently outperforms other methods in both offline and online settings.
Comparison with Other Baselines. We compare our method with more methods, including Novel Class Discovery (ComEx (Yang et al., 2022a), IIC (Li al., 2023a)), inheritable model (Kundu et al., 2020), SFDA² (Hwang al., 2024), and CODA (Chen al., 2023a). Results in Table 11 show that ADA outperforms these methods three benchmarks.
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Table 10: Results on VisDA.
Methods
acok
OCCu
hts
Tent
55.3
40.2
16.6
OSTTA
46.8
72.0
56.7
UniEnt
52.3
70.1
59.9
ART
51.3
77.2
ADA (Online)
52.9
77.1
62.7
ERM
61.2
39.9
48.3
SHOT
51.5
59.8
55.3
AaD
48.0
87.3
61.9
OneRing
62.1
59.5
60.8
ADA (Offline)
53.0
80.1
63.8
Table 11: Comparison with more methods.
OFFICE HoMe
OFFICR-31
PACS
Methods
hs
aCO2
hus
0C0k
hss
ComEx
57.8
63.9
57.1
77.6
68.6
72.6
50.8
59.8
53.0
ICC
57.4
71.3
61.7
76.6
75.3
75.8
48.6
56.0
19.4
Inheritable
67.6
65.0
65.3
84.4
77.0
80.2
51.5
59.8
54.0
SF(DA)²
64.7
69.0
65.7
85.0
74.5
79.3
64.0
49.6
55.1
CODA
62.4
73.3
66.7
83.7
78.2
80.5
53.3
64.4
57.6
ADA (Oflline)
63.1
76.7
68.4
84.5
81.3
82.7
53.9
71.7
58.7
B.2 GradCAM Visualization
The additional GradCAM visualizations (Selvaraju et al., 2017) of ADA and ERM are shown in Figure 7. Consistent with the result in the main paper, it can be observed that the hot zones activated by ADA are more accurate and comprehensive compared to ERM. Hence, ADA provides a more thorough understanding of the images in the open-set and distribution-shitted context. Unknown Class
Known Class
ADA (Ours)
ERM
Figure 7: GradCAM visualization of ADA and ERM.
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B.3 Visualization of Condensed Dataset
Figure 8 shows the condensed images of three datasets. We can see that these images look like noise images and do not show any resemblance to the original training data visually. Thus, it prevents information leakage of the source domain training data to some extent. Morcover, as claborated in our main paper, it preserves information that facilitates our model adaptation process.
clock
backpuck
buckpack
bike
calculator
batteries
headphone
keyboard
bike
laptop
bottle
monitor
bucket
mouse
calculator
mug
calendar
pen
candles
(b)
Office-31
chair
clipbourd
dog
computer
elephant
couch
giraffe
certain
guitar
(a) Office-Home
(c) PACS
Figure Visualization of condensed data of OFFICE-HOME (Artistic), OFFICE-31 (Amazon), and PACS (Photo). Fach shows 10 images per (known) class.
C Limitations
Our approach has limitations. Firstly, ADA treats all open classes as a single category (the unknown class (IC,) +1)-th dimension in the classifier), which restricts its broader applicability. In situations that require the identification of individual open classes, ADA's effectiveness the source domain data to smaller dataset. Therefore, the success of ADA depends the advancements in dataset condensation techniques. As the number of images and categories increases, the complexity of the dataset condensation process also rises. However, anticipate that improvements in dataset condensation techniques, along with advances in deep learning hardware, will help address this challenge.
assured. Secondly, ADA relies on condensing
20


Reviews:
[{"review_id": "3N5yBstHky", "reviewer": "Reviewer_Scfk", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths:\n\n1. The authors’ tackle the problem of open-set test time adaptation which is quite practical but underexplored.\n\n2. The idea of extracting features of new classes from the source domain images and using it for training the model to be able to identify unknown samples is interesting.\n\n3. The style injection from the target domain into the condensed data helps in providing supervision for adapting the model at test-time.\n\n4. The domain alignment loss further makes the model insensitive to the domain variance.\n\nWeaknesses:\n\n1. The authors’ use a domain alignment loss where for the granular features from condensed data the probability that they belong to the target data is maximized. If the binary classifiers gk and gu are trained with this loss, won’t they just learn a reverse mapping? I am not sure if I am missing something here.\n\n2. The accuracy of the proposed approach is lower than other methods for known class identification. Is this because of the unknown improvement loss which increases the probability of the sample being unknown even for known class samples? Can the authors explain why the known class performance is not good enough?", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The paper proposes a novel technique for open-set model adaptation. In this setting, a model is first trained on the source domain and then adapted to the target domain which can also have certain new classes. The authors propose a two-stage framework: ADA, Activate and Adapt for effective model adaptation. They extract new class information from the source domain data and use it for training the model to be able to identify unknown samples. To prevent forgetting during adaptation, they condense the source domain data into a small dataset. Furthermore, they adapt the model to the target domain at test time by injecting target style information into the condensed data providing supervision for effective recognition of both known and unknown target classes. Moreover, a domain alignment loss is introduced to make the model insensitive to the domain variances.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "1. It’s not clear how the authors get the condensed dataset. It would be nice if they can add further details in that subsection.\n\n2. The methods used for comparison are not the most recent ones. The authors should consider adding comparisons with some more recent approaches like:\n\nHwang, U., Lee, J., Shin, J. and Yoon, S., 2024. SF (DA) $^ 2$: Source-free domain adaptation through the lens of data augmentation. arXiv preprint arXiv:2403.10834.\n\nKundu, J.N., Kulkarni, A.R., Bhambri, S., Mehta, D., Kulkarni, S.A., Jampani, V. and Radhakrishnan, V.B., 2022, June. Balancing discriminability and transferability for source-free domain adaptation. In International conference on machine learning (pp. 11710-11728). PMLR.\n\n3. DA papers usually also experiment on VisDA and DomainNet datasets. Can the authors also add experiments on some of these datasets in the paper?\n4. It would be nice to also have an ablation to see how the unknown improvement loss affects the known class accuracy.", "broader_impact_concerns": "NA", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "UOMeWgdpqn", "reviewer": "Reviewer_xmJo", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "**Strengths**\n\n1. The paper addresses an important practical problem that combines domain shift with open-set challenges\n2. The two-stage approach offers a novel solution that doesn't require predefining thresholds for unknown detection\n3. The experimental evaluation is comprehensive across multiple datasets and settings (online/offline)\n4. The method outperforms previous approaches in both settings on all benchmarks\n\n**Weaknesses**\n\n1. The explanation of how unknown classes are extracted from source domain data is unclear. The paper claims to utilize \"rich semantics\" in source domain data, but the mechanism by which misclassified granular features represent unknown classes needs stronger justification. How do we know these misclassifications correspond to potential unknown categories rather than just difficult examples of known classes, and at what points in the training do we define them as unknown? \n\n2. The decomposition of feature maps into \"granular features\" (Equation 1) may be problematic. For ResNet-50, the penultimate layer typically has 7×7 spatial resolution, meaning each granular feature corresponds to a 32×32 pixel region in the original image (assuming 224×224 inputs).  \n\n3. The statement \"Source domain data is often unavailable during adaptation due to safety and data privacy concerns. Most previous methods simply discard it during adaptation\" is confusingly written. It implies methods discard data for safety/privacy reasons, but in reality, source-free methods are designed specifically to operate without source data because of these constraints.\n\n4. Please reformulate  \"after forwarding the fixed feature extractor f.\"\n\n5. The definition of \"upper and lower parts\" of the feature extractor is ambiguous: \"We denote f = f1 ◦ f2, where f1 and f2 denote the upper and lower parts of f, respectively.\" Which layers specifically constitute f1 versus f2?\n\n6. The Source Condensation component introduces additional computational overhead and complexity. Is this condensation step truly necessary? The paper claims it addresses privacy concerns, but creating and transferring condensed data still requires additional compute and storage resources. Could the method be simplified to work directly with the trained source model without this intermediate representation? One may claim that the condensed dataset can be reconstructed using inversion methods.\n\n7. The unknown improvement loss in Equation 3 uses L2-norm regularization without sufficient justification over simpler alternatives like label smoothing.\n\n8. In Tables 2-3, the online h-score is surprisingly close to the offline h-score on Office-Home, for example. Could you elaborate on why this is the case? \n\n9. The visualization in Figure 4(b) compares ADA's GradCAM with ERM, but ERM lacks unknown-class training, making this comparison potentially misleading. How do you actually do the ERM GradCAM if you don't have the unknown class?\n\n10. The related work section should include recent advances in source-free open-set domain adaptation, such as GLC, Lead, as well as a discussion of inheritable models for open-set domain adaptation.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper addresses the challenging problem of Open-Set Adaptation, where a model trained on source domain data containing only known classes needs to be adapted to a target domain with distribution shift and new unknown classes. The authors propose a novel two-stage framework called Activate and Adapt.\nIn the first stage, the model extracts unknown class information from the rich semantics within the source domain data and condenses the source dataset while preserving domain information.\nIn the second stage, the model is adapted to the target domain by injecting the target domain style into the condensed dataset and decoupling domain alignment for known and unknown classes.\nThe framework is evaluated on three standard benchmarks: Office-Home, Office-31, and PACS, in both online and offline settings.", "claims_and_evidence_yn": "No", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "1. Please experiment with higher-resolution backbones (e.g., ViT).\n2. Compare your fine-grained method with the one from inheritable models for open-set domain adaptation.\n3. Quantify iteration costs in online settings between your method and others.\n4. Please see weaknesses above", "broader_impact_concerns": "N/A", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "ATTAZOsXYj", "reviewer": "Reviewer_nB99", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths:\n1. The paper is well-written and timely. Stable test time training is an important problem statement, especially in the age of large models with even longer training times.\n2. The motivations of the proposed components are intuitive and reasonable.\n3. The ablation study in Table 4 is extensive.\n4. Limitations are discussed in the Appendix\n\nWeakness: \n1. The experimental section has supporting evidence that the methods are working as expected and better than existing methods. Your method handles two things domain shit and novel class addition. I think comparing it with existing novel class discovery methods will also be useful in showing the relative performance of your method, even if the train and test protocol are not completely identical.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The paper presents a generalization of the test time adaptation problem. The test domain can undergo both domain shifts and additional classes during test time in their version. To handle this, they utilize the semantic information in the source classes to find the new classes in the test domain. At the same time, they also condense the information in the source domain to use it as a remembering mechanism during adaptation.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "1. Additional experiments with Novel class discovery methods such as [1] and other modern baselines.\n2. Instead of using condensed data, can you subsample from the source dataset and use that as your condensed dataset and then report the results? This will help validate the utility of data condensation. \n3. If the above is done, then use it to do OMA. After that, it will effectively serve as doing OMA without UASC, and I believe it will add proof to the independent usefulness of the components.\n\n[1] Vaze, Sagar, et al. \"Generalized category discovery.\" Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022.", "broader_impact_concerns": "There are no adverse broader impact of the work.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Decision: Accept (poster)