PRISM: Progressive Robust Learning for Open-World Continual Category Discovery
Authors: Wei Feng
Sijin Zhou
Yiwen Jiang
Zongyuan Ge
Venue: ICLR 2026 Poster (2026)
Keywords: Continual Category Discovery
Generalized category discovery
Domain shift
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
TL;DR: We propose PRISM, an adaptive framework for Open-World Continual Category Discovery that leverages spectral separation, sparse assignment matching, and invariant knowledge transfer to robustly recognize known classes and discover novel ones.

Abstract: Continual Category Discovery (CCD) aims to leverage models trained on known categories to automatically discover novel category concepts from continuously arriving streams of unlabeled data, while retaining the ability to recognize previously known classes. Despite recent progress, existing methods often assume that data across all stages are drawn from a single, stationary distribution—a condition rarely satisfied in open-world scenarios. In this paper, we challenge this stationary-distribution assumption by introducing the Open-World Continual Category Discovery (OW-CCD) setting. We address this challenge with PRISM (\underline{P}rogressive \underline{R}obust d\underline{I}scovery under \underline{S}trea\underline{M}ing data), an adaptive continual discovery framework consisting of three key components. First, inspired by spectral properties, we develop a high-frequency-driven category separation technique that exploits high-frequency components—preserving more global information—to distinguish known from unknown categories. Second, for known categories, we design a sparse assignment matching strategy, which performs proximal sparse sample-to-label matching to assign reliable cluster labels to known-class samples. Finally, to better recognize novel categories, we propose an invariant knowledge transfer module that enforces domain-invariant category relation consistency, thereby facilitating robust knowledge transfer from known to unknown classes under domain shifts. Extensive experiments on the SSB-C and DomainNet benchmarks demonstrate that our method significantly outperforms state-of-the-art CCD approaches, highlighting its effectiveness and superiority.

Published as a conference paper at ICLR 2026
PRISM: PROGRESSIVE ROBUST LEARNING FOR OPEN- WORLD CONTINUAL CATEGORY DISCOVERY
Wei Feng!.3 Sijin Zhou Yiwen Jiang'
Zongyuan Gel.2+
AIM for Health Lab, Monash University
2 Airdoc-Monash Monash Research, Monash University
{wf02429, sjzhoul 995 @mmaal com, {yiwen jiang, zongyuan. mwngyuan.geeymonashhcco
ABSTRACT
Continual Category Discovery (CCD) aims to leverage models trained on known categories automatically discover novel category concepts from continuously arriving streams of unlabeled data, while retaining the ability to recognize pre- viously known classes. Despite recent progress, existing methods often assume that data across all stages are drawn from a single, stationary distribution-a condition rarely satisfied in open-world world scenarios. In this paper, we challenge this eentionar-ddisributte Category Discovery (OW-CCD) setting. We address this challenge with PRISM (Progressive Robust discovery under StreaMing data), an adaptive continual dis- covery framework consisting of three key components. First, inspired by spectral properties, we develop high-freqqency-driine category separation technique that exploits high-frequency
assumption by introducing the Open-Wort World Continual
peapoonnntt--reseervee more global information-to distinguish known from unknown categories. Second, for known categories, we design a sparse assignment matching strategy, which performs proximal sparse sample-to-label matching to assign reliable cluster labels to known-class ples. Finally, to better recognize novel categories, we propose an invariant knowl- edge transfer module that enforces ammain-invarian category relation consistency, thereby facilitating robust knowledge transfer from known to unknown classes un- der domain shifts. Extensive experiments on the SSB-C and DomainNet bench- marks demonstrate that our method significantly outperforms -fate-of-the-ar CCD approaches, highlighting its effectiveness and superiority.
INTRODUCTION
Visual concepts in the real world are open ended and continually evolving, far exceeding any pre- defined category set (Feng al.
2023a 2022 Ju al. 2024 Jiang et al. 2025). Although deep
learning has achieved remarkable progress in visual recognition (Hang et al. 2020: Jiang et al. 2025
Feng et al. 2025a Hakeem al.
2022), most advances rely on closed-worid assumptions- models
are trained on fixed label spaces and therefore struggle when encountering previously unseen cate- gories. Humans, by contrast, naturally generalize from prior knowledge to organize and recognize new concepts. This discrepancy has led to growing interest in category discovery (Vaze et al. 2022
Han et al. 2021 Wen al.) (023).
Early studies formulated this task as Novel Class Discovery (NCD) (Han et al., 2021 Feng et al., 2023aj, where all unlabeled samples belong to novel categories. To better reflect realistic conditions, Generalized Category Discovery (GCD) (Vaze et al. 2022, Wen et al. 2023 Feng et al.) 2025b) extended this setting by allowing unlabeled data to contain a mixture known and unknown classes, requiring models to both identify known classes and cluster new ones. However, both NCD and GCD are built upon static datasets and assume simultaneous access to labeled and unlabeled data. These assumptions diverge from real-world conditions, where data typically arrive continuously evolving, unlabelled streams. As a result, NCD and GCD overlook the dynamic nature of open environments and fall short of modeling realistic data-stream scenarios.
"Corresponding author.

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Published as a conference paper at ICLR 2026
To close this gap. the community has recently moved toward Continual Category Discovery (CCD) (Park et al. 2024, Cendra et al., 2024), which integrates continual learning with category discovery. CCD aims to progressively identify emerging categories while preventing catastrophic forgetting of previous knowledge. Despite this progress, existing CCD settings commonly assume that data each stage comes from a single, fixed domain-an assumption rarely met in open envi- ronments. In practical scenarios, samples may originate from diverse sources or shift across domains while new categories appear. For example, an online platform may continuously receive animal im- ages from different cameras or users; as the domain (e.g., lighting, style, device) changes, rare species can emerge concurrently with existing categories.
Motivated by these limitations, we propose a more realistic setting called Open-World World Continual Category Discovery (OW-CCD). In OW-CCD, models must automatically discover known and unknown categories from unlabeled streams without assuming domain consistency. This introduces several challenges. First, difficult to preserve recognition ability for known categories under distribution shifts, as existing CCD methods are not designed to handle domain variations. Second, the model must continually discover emerging categories in dynamic, non-stationary streams. Tra- ditional domain adaptation techniques are unsuitable, they often assume overlapping label spaces; naive alignment may even cause negative transfer and suppress novel-category discovery. Moreover, most adaptation methods focus aligning known classes, offering little guidance for discovering unseen ones.
To address these challenges, we introduce PRISM (Progressive Robust discovery under StreaMing data), new adaptive divide-and-conquer frame- work for OW-CCD. Our design is inspired by spec- tral analysis (Fig. c)): tigh-frequency compo- nents tend to capture domain- invariinn global se- mantics (e.g.. structures), whereas low-frequency components encode domain-dependent details (e.g., style). Leveraging this insight, we develop a high- frequency-driven category separation module to dis- tinguish known from unknown samples under do- main shift. To ensure reliable recognition of known categories, we further propose a sparse assignment matching module based on proximal optimal trans- port, producing stable and sparse pseudo-labels. Fi- nally, following the core principle of category dis- covery- transfrrnng knowledge from known to un- known classes through semantic relations-we -e in- troduce an invariant knowledge transfer (IKT) mod- ule. Instead of relying on domain-specific cues that may distort associations, IKT represents the rela- tions between known and unknown classes as rank- ing permutations. These permutations converted into ranking probability distributions and enforced to remain consistent across domains. This rank-hased formulation ensures that semantically closer classes contribute stronger knowledge transfer; for instance, in CUB, closely related bird species such as Indigo Bunting and Lazuli Bunting share meaningful high-level semantics despite subtle visual differences. Maintaining these relationships across domains enables stable transfer and facil- itates robust discovery of novel categories.
(a) Image
(b) Low-frequency
5C-
Jnknow Canopors
Humbarr of Sampien
Crean Categery
3C-
0.00
0.25
0.50
3.75
1.0
350)
(c) High-frequency
(d) Density scores
Figure 1: (a-c) Visualization of low- frequency and high-frequency components of the images. (d) Visualization of the den- sity scores S(z), where the density distribu- tion exhibits a clear bimodal pattern corre- sponding to known and unknown samples.
In summary, our contributions are as follows: (1) we introduce the Open- World Continual Category Discovery (OW-CCD) setting and present PRISM, an adaptive divide-and-come ann-conquer framework; (2) category separation strategy to distinguish known and unknown samples under domain shifts; (3) we design a sparse assignment matching module based on proxi- mal optimal transport for reliable pseudo-labeling of known categories; (4) we develop an invariant knowledge transfer module that preserves semantic relations between known and unknown cate- gories across domains for stable discovery; and (5) through extensive evaluation on the SSB-C and DomainNet benchmarks, we demonstrate that PRISM achieves strong effectiveness and robustness, consistently outperforming sfate-of- oftth-of-teeart
we propose wew---feuee---rriveeee
the-art CCD approaches.
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2
RELATED WORK
Category Discovery Category discovery aims to leverage knowledge from labeled classes to iden- tify novel concepts in unlabeled data. Novel Class Discovery (NCD) was introduced to address sce- narios where all unlabeled samples belong to unseen categories. Early methods adopted two-stage pipelines, such as AutoNovel (Han et al. 202T), which transfers knowledge through self-supervised learning with ranking statistics. In contrast, unified end-to-end approaches (Fini et al. (202T) directly integrate representation learning and clustering into single stage. Later extensions address sample imbalance by designing self-cooperation mechanisms that leverage both known and novel represen- tations for mutual learning (Wang et al. 2024c), or enhance class-level knowledge transfer through symmetric relationship modeling and pairwise consistency constraints (Zhou & Chen 2025). Gen- eralized Category Discovery (GCD) relaxes this setting by mixing known and unknown categories.
Early frameworks combined supervised and unsupervised contrastive learning with clustering (Vazel et al. 2022 Feng & Ge 2025), while SimGCD (Wen et al. 2023) introduced a parametric classifier for efficiency. More recent work explores hierarchical modeling (Liu al. 2025b), prototype- learning (Ma et al. 2025), and reciprocal learning with distribution regulanzztiion (Liu et al. (202aa). Beyond these directions, some studies investigate domain- level extensions, addressing category dis- covery under domain shifts (Wang et al., 2024a [Rongali et al. 2024). Continuous Category Dis- covery (CCD) further considers streaming settings. Methods such as grow and merge (Zhang al., 2022), energy-guides discovery (Park et al.] 2024), Gaussian mixture prompting (Cendra et al., 2024]. , and Bayesian inference (Dai & Chauhan (2225) have been proposed to tackle class discov- ery streaming data, though they often operate under the simplifying assumption of single-domain streams.
Domain Adaptation Domain adaptation tackles distribution gaps between labeled source and tar- get domains. Unsupervised domain adaptation (UDA) leverages labeled source and unlabeled target data, typically by learning domain-invariant representations. Discrepancy-based methods minimize
moment mismatches (Sun & Saenko 2016: Long et al. 2015 Tzeng et al. 2014 Feng et al. [022
20236), while adversarial approaches employ domain discriminators (Saito et al. 20 8a Sankara
narayanan et al. 2018). Transformer-based backbones (Dosoviskkiy et al. 2020) have also been explored with attention-driven alignment (Sun et al., 2022] Xu et al.] (021). Source-Free Domain Adaptation (SFDA) removes source data access. Representative works include prototype transfer (Chidlovskii et al. 2016), iterative pseudo-labeling (Liang et al. 2019). SHOT (Krause et al. 2010 Shi & Sha 2012). and neighborhood regularization (Yang et al.] 202T). Beyond this, Open-Set Domain Adaptation (OSDA) addresses unknown target categories. Strategies include confidence thresholding (Saito et al. 2018b), progressive separation (Liu et al., (019)) and causal adjustment
(Li et al. (023b). While OSDA extends domain adaptation to more realistic scenarios, most existing methods remain centered on classifying known categories and pay limited attention to systematically exploring the unknown label space.
METHODOLOGY
3.1 PROBLEM STATEMENT
Open- World Continual Category Discovery (OW-CCD) involves one base learning session followed by online continual discovery sessions. In the base session, we are provided with a labeled dataset
D' = (I, J. consisting of N' labeled samples drawn from a known category space C². In each subsequent online discovery session, an unlabeled data stream Dj = {
N - introduced in- crementally. This stream not only contains samples from the previously seen known categories, but also includes samples from novel categories CH in session 1. Moreover, these samples may originate from domains that differ from the domain distributions observed in the base session, thus introduc- ing additional domain shift. The goal of OW-CCD is to enable the model to robustly discover novel concepts from the dynamic unlabeled data stream in an online manner, while simultaneously main- taining recognition capability for known categories and alleviating the impact of distribution shifts much as possible.
Fig. illustrate the overall framework of our proposed PRISM method. In the base session, we first pre-train model 0 {f.9) on the labeled dataset using cross-entropy loss, where f(-) denotes
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Bose
High-rrqqency Companants
Hgtt-frepanccy--ee Sategery Separation
Knewn Gatopery
Highfrrquancy Fectures
Pretetypes
Krewn Ceteghry
Labeled
Unioteled Conagery
Festur netture Emscoder
Supervised
Usizown Category
Krown
Domaan
Farur
foutis
Ligh-frqqanyy Components
Domain
Offinns Madel
Unlobeled Uniowsn
Uniobeled Known dategary
Unlobeled Unkkowen Catagery
Sperse Assigement Matching
Closs
Class 8
Inverient Knawledge Transfer
airpione recket
raceoon Bear
O
O
Cluad
Class A
Plochett-Lucee (PL)
Uniobeled Unknowm Categery
Closs
Resieall Samplng
Uniebeled Knean Categary
Porling Pormattiion
Renling Pemuuctio
Figure The overall framework our proposed method.
the feature extractor and g(.) is the classifier head. This provides discriminative representations a foundation for subsequent discovery. In the online discovery stage, we then introduce three key innovations. First, the High--Frqqencc-Driin ically separate known and unknown categories by exploiting high-frequency information in images. Second, the Sparse Assignment Matching (SAM) module assigns reliable cluster labels to samples from known categories. Finally, the Invariant Knowledge Transfer (IKT) module captures robust category associations across different domains, thereby enabling stable and effective novel category discovery.
Category Separation (HCS) is employed to automat-
3.2 HICCH-FREQUENCY-DRRR CATEGORY SEPARATION (HCS)
Since direct distribution aligmment may lead to negative transfer. we adopt swvide-and-conquee strategy. Below, first describe how extract the high- and low-frequency components of images, and then employ the HCS module for category separation. Given an input image I; E IR x XC where H, W, and C' denote the height, width, and number of channels, respectively, first trans- form it into the frequency domain using the Discrete Fourier Transform (DFT):
#-1 W-1
F(x:)(u,e,c) = Σ Σ
ar w. c)
-2
(1)
A=
where ;²= -1 and denote the spatial frequency coordinates, and indexes the RGB channels. Following common practice, the low-frequency components are shifted to the center of the spectrum for convenience. To separate low- and high-frequency information, construct binary mask M E R
[1, if max([u 11 0 otherwise,
W S
min (H.W)
Ma
where r controls the relative size of the mask. The low- and high-pass frequency components are then obtained
F()))) = M O F(z)) F"(,,) = (I M) O F(x,),
(3)
where @ denotes element-wise multiplication, and F and jhh are the masked low- and high- frequency spectra obtained from the DFT F(x;). Finally, inverse DFT is applied recover spatial representations of low- and high-frequency images: x -F (x,)) I FFF(((
We focus on the nigh-frequency component zi of the unlabeled data, as gigh-frequency cues often contain discriminative structural information that helps distinguish known from unknown categories (see Fig. [a-c)). These components are fed into the pre-trained feature extractor f from the pre- vious stage obtain high-frequency representations. Based these representations, define

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)(a") density scoring function: S(x) of maxe (T Hellll where 6. denotes the prototype of the
known class from the previous stage, and v(-) is a min-max normalization mapping scores to [0, 1]. Intuitively, S(x) measures the maximum similarity between the high-frequency representa- tion of unlabeled sample and known category protolypes: larger values indicate that the sample is closer to known classes, while smaller values imply it may belong to an unknown class. As shown in Fig. dd), we empirically observe that the distribution of ((x) often exhibits a bimodal shape, corresponding to known and unknown samples, respectively. To automatically separate them, we model the distribution of S(x) using a two-component Gaussian Mixture Model (GMM): P(x) TTx) N(x (Inoo Amx) + (x)) J (z Tink). where xxx) is the posterior probability of being assigned to the known component, estimated using the EM algorithm. NNx IM, o²) denotes
Gaussian distribution with mean μ and variance a², and (Mano: kmo) and (Punk o correspond to the parameters of the known and unknown components, respectively. Finally, at each online session we split the incoming data stream D into known and unknown subsets:
Di, = {0 x E D. A (x)) 2 0.5), Di,unk = {or x Dr A T(x) < 0.5).
(4)
This separation provides a reliable mechanism for dynamically identifying known-lik like and unknown-like samples, which lays the foundation for subsequent discriminative learning and novel category discovery.
3.3 SPARSE ASSIGNMENT MATCHING (SAM)
Since we have already obtained the known-category samples via the proposed HCS module, we next explore the possible labels for these known sam- ples Tino Di K, As Tkmo shares the same semantic space with the known prototypes from the previous stage, domain adaptation techniques can be employed to uncover the latent alignment. In this process, Optimal Transport (OT) provides a powerful tool to automatically discover proper cample-prototype correspondences across domains, thereby alleviating domain discrepancies (Courty et al., [014] |Flamary et al. (0166). However, directly solving the OT problem with linear programming in- curs a prohibitive computational cost (Xu & Dan 2025]; even though the entropy regularization improve efficiency, it usually yields overly dense transport plans, leading to inaccurate assignments illustrated in Fig. a). To overcome this limitation, we propose Sparse Assignment Matching (SAM) scheme by incorporating an &g-nomm proximal term. The optimization objective is formu- lated as:
tott detee de Livoe opecdt
Figure 3: Toy illustration of our pro- posed SAM method. Conventional Sinkhorn and fz-regularized solvers tend to produce smooth yet dense couplings, whereas the proposed SAM yields sparser transport plan with better performance.
Ne Ch min ((>)) = Σ YEA
2 (76 1
No Cis +
(5)
i=1
where No. denotes the number of samples Di, Ct-1 denotes the number of known classes
from the previous stage. The cost matrix is defined as Cij = log (g (Z))))))))) A smaller value of Ciy indicates that sample Ki,kmo has higher probability of belonging to the j-th class, while larger cost suggests a less plausible assignment. The second term 5/2 Σ,, (7k5 7/ (2)) acts an bg- based proximal regularizer, which suppresses oscillations across iterations and enforces sparse and et
stable solutions. Meanwhile, A = E
j= Ki = à; = 1, E. Att.ko %i = b N.
टt- Try 2 0} is
the feasible set. We initialize Tij - for the following iterations. To avoid directly handling the constrained problem, we first obtain the Fenchel- dual formulation of the original problem:
max St
No. Σ
Σ
1
p,bb
Nr C Σ
Σ
1.. +
4
C3s
(6)
t=1
1=1
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where op and & denote the Lagrange multipliers, and [z] (max(0, 2))² denotes the truncated quadratic operator ensuring non-negativity. Cij - C5 - - Note that the original con- strained OT problem in Eq. (5) is transformed into an unconstrained optimization over (b))p), which computationally more efficient. The detailed optimization procedure for solving and i is provided in the Appendix. Then the transport plan 7((+2) can be updated in closed form as: (1+1) max 0, (ths + Ly + + ( Cty)/E}.
Tiy
alignment, we assign reliable pseudo labels to the known samples Vano E -Dx Moreover, compared with the standard (z-regularized OT, the proposed SAM produces significantly sparser and clearer assignment pat- terns, as depicted in Fig. (c), which contributes to more trustworthy pseudo-label generation.
With the optimal transport plan 7 obtained from sparse
3.4 INVARIANT KNOWLEDGE TRANSFER (IKT)
For unknown category discovery, build the core idea of category seanvvey-ttannferriing cat- egory knowledge from known to unknown classes by exploiting their semantic associations. Under domain shift, however, such associations may be distorted by domain-specific style factors, lead- ing the model to capture spurious rather than genuine relations. We argue that effective discovery should instead depend on conaaininvariant category associations that reflect stable semantic struc- tures. To this end, we propose an Imariant Knowledge Transfer module, which explicitly models the relationships between unknown samples and known prototypes across domains and enforces their invariance, thereby facilitating the transfer of authentic semantic knowledge. Specifically, in each previous stage we pre-compute the frequency-domat
statistics of the known domain. For this pur- pose, we apply the discrete Fourier transform as in Eq. (T) and decompose each spectrum into low- and figh-freeuency components (F', according to Eq. (3). Inspired by recent works (Li et al,, 2022 [Wang et al. 2022) that employ spatial feature statistics to characterize style, we characterize the low-freyuency spectrum by the shannel-wiss mean and standard deviation:
M(F)) HW F(u,v,,)) oFF)
Fl,, uFF))
HW
We assume that the distribution of these statistics follows a Gaussian distribution, and estimate their variances across the data from the previous stage:
N Σ
(F)
EE(F) N Σ -(F))-B((F)))))
where N1-1 denotes the number of samples from the previous stage. We then sample perturbed low-frequency statisties from these Gaussian distributions:
M(F) ((F)) t (F)), Eu 10,,1, =(F)=o(F)) te (F), e, 1).
For unknown sample af aunk in the current data stream, we extract its low-/tigh-frequueccy com- ponents (Fi Fih F/inns) via Eq. (3) and reconstruct the low-frequency spectrum with the sampled statistics:
F!
Kunk ((FF + ((F)). oFF. ((FF,,n)
Bankk ((F))
(10)
Finally, we combine Fi trum F(r),ua)) and apply inverse DFT to obtain the augmented sample at Ft Then, for each unknown-category sample offunk and its style-transferred counterpart T.,unk- we extract their feature representations: z/,umk = [t E /(S) Let (er ( denote the set of known class prototypes from the previous stage. For each unknown sample, we convert its cosine similarities to these prototypes under both the original and style-transferred views into the strength parameters of the Plackett-Luce (PL. model: Ky = exp( cos(2i,unk e(-1)) Ri, = exp( cos(22, (2),unkk e/-l)) As the number of known classes grows, enumerating or even implicitly handling all C permutations becomes infeasible. Therefore, following standard practice in listwise ranking (e.g., ListMLE (ia
(unk with the original high-frequency part Fhh funkk to form an augmented spec-

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Published as a conference paper at ICLR 2026
et al. 2008 ) we adopt the factorized PL likelihood, whose sequential decomposition provides an analytically exact and computationally tractable form of the model. Given Ri = :...1....,,..c--. the likelihood of permutation ç is:
P ki) = II
Ki,g(k)
(1I)
1=1 =k= (ig(R)
where E(k) denotes the prototype placed at position For illustration, when C and Ri.
This example reflects the inherent sequential normalization of the PL model and does not require constructing or summing over all permutations. To ensure that these associations remain consistent across domains, we enforce view-invariant rank- ing by aligning the PL likelihoods from the original and style-transfent views through divergence minimization:
& c)., K4)= Reatx,,tt, Ku,a- Ri,e
Ki.b Ki.s
Frank
(PL(P(-1)) x4), ((-1R.)):
(12)
Ne,unk
where Nukk denotes the number of unknown-category samples, and (KI. is the Kullback-Leibler divergence. Crucially, the KL divergence between two PL distributions also decomposes into sum over the corresponding local log- pog--obabilltty terms, meaning that our implementation optimizes the exact computable KI. induced by the factorized PL model rather than an approximation over the full permutation space. By enforeing agreement between the two factorized PI. likelihoods, the model preserves the global relative ranking between unknown samples and known prototypes, providing strong structure-awarr regularization signal for category discovery under domain shift.
3.5 ONLINE ADAPTATION
Following the setup of (Pak et al., 2024), assign labels to known and novel samples with dif- ferent strategies. For data from known categories, we employ the SAM module to generate reliable pseudo-labels. For previously unseen categories, i.c., samples not belonging any known class, we adopt Affinity Propagation (Frey & Dueck 2007) to automatically infer cluster memberships. As non-paraan con-paraettric clustering algorithm. Affmmtyy Propagation iteratively exchanges messages be- tween samples based on pairwise similarities, thereby estimating the optimal number of clusters without requiring as a prior, which is particularly suitable for open-world scenarios where the number of novel classes is unknown. The inferred clusters are then used to dynamically expand the online classifier, enabling the integration of emerging categories. During online learning, we combine mecudo-labeled known samples with clustered novel samples and incrementally update the model using a standard cross-entropy loss, allowing the system to acquire new semantic knowledge without revisiting past data. The overall optimization objective of method can be formulated Lunat = Lee + x₁ Crank, where Lce denotes the cross-entropy loss computed on pseudo-labeled data and A is balancing hyperparameter.
4
EXPERIMENTS
4.1
EXPERIMENTAL SETUP
Dataset We evaluate our method on two representative benchmarks: the Corrupted Semantic Shift Benchmark (SSB-C) (Wang et al.] 2024a) and DomainNet (Peng et al.) 2019). SSB-C extends the Semantic Shift Benchmark (SSB) with nine corruption types at five severity levels, covering three fine-grained datasets. This benchmark provides challenging platform assess robustness under both semantic and visual perturbations. DomainNet is large-scale dataset with six diverse domains, featuring hundreds of categories and substantial domain gaps. Following (Wang et al.) 2024a), we use the original datasets in SSB-C as known domains and their corrupted versions as unknown domains. For DomainNet, the Real domain serves as the known domain, while each the remaining domains is treated an unknown domain in turn; also evaluate a mixed setting where all Ron-Real domains are merged into one unknown domain (details in the Appendix). The category split follows (Cendra et al. 2024): subset of labeled known classes from the known domain is used in the base session, and subsequent sessions sequentially introduce new streams containing both known and novel categories. Importantly, each session includes samples from both known and
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unknown domains, simulating realistic scenarios with simultancous category expansion and domain shift.
Implementation details We adopt ViT-B/16 Dosovitskiy et al. 2020) as the backbone, pretrained with DINO (Caron et al., 2021 Oquab et al. 2023). Following prior work (Wen et al., 2023 Park al., 2024), only the final transformer block fine-tuned each stage using SGD for 30 epochs with batch size of 128. The initial learning rate is 0.1 and decayed to x 1 via cosine annealing, and weight decay is fixed at 5 x 10-5. We set the trade-off parameter A = 1, the number stages T = 3. and the binary mask ratio 0.3. The proximal strength parameter in the SAM module fixed to 0.5. All experiments are repeated with three random seeds, and averaged results are reported. Models are implemented in PyTorch and trained eight NVIDIA RTX 4090 GPUs.
Evaluation protocol. We adopt continual clustering accuracy (cACC) (Cendra et al. 2024) as our primary evaluation metric. cACC measures the average clustering performance over all sessions up to stage i, defined as: cACC, = Ek ACC. Here, ACC2 denotes the clustering accuracy on the test dataset of session k. Following (Wang et al. 2024a, Vaze et al.) 2022), clustering accuracy (ACC) is defined by comparing the ground labels y/i with the predicted cluster assignments ÿú:
ACC = Dy Σii T(y; = g" ((:)), where g" denotes the optimal permutation mapping predicted clusters their ground-truth counterparts. We report cACC results both known and unknown domains, and further break them down into All, Old, and New categories for a comprehensive eval- uation.
Table I: Clustering performance on DomainNet benchmark. We use Real as the known domain and each of the remaining domains as the unknown domain. We report the average All / Old / New accuracy across all stages for both domains.
Rest
Saiccn
48.4
63.2
413
22.6
224
11.
662
16.
363
235
42
571N4
46.3
64.5
42.
241
235
343
494
033
33.
39
1.2
96
34
525
248
49
46
55
431
833
250
11.
-93
8.8
229
26A
268
8.6
286
66.2
441
23.
346
358
641
461
111
183
563
23
43
624
06M
Hope
16.5
68.1
417
14
121
149
115
413
113
137
H6
914
78
45
674
371
94
BA40CD
133
411
161
368
382
06.5
11.2
112
423
363
111
11/
116
944
194
191
111
63
Table 2: Clustering performance SSB-C benchmarks, Each dataset contains both Original and Corrupted settings, and we report the average All / Old New accuracy across all stages for both domains.
Methods
Original Old
CUB-C
Comppted Old
Original Old
Stanford Cars-C
Cormupted
Original
FGVC-Aircrafi-C
Corrupted Old
All
New
All
New
All
New
All
Old
New
All
Old
New
All
New
GCD
29.4
47.7
23.4
26.8
45.9
20.1
26.4
56.1
21.5
22.3
43.1
11.2
27.7
33.6
24.9
28.8
41.4
28.8
SimGCD
SPTNet
27.8
45.2
22.0
25.1
44.2
18.1
24.9
55.0
20.3
21.1
41.6
9.9
26.1
31.2
23.3
26.9
39.5
26.7
RLCD
29.1
46.8
23.8
26.2
45.3
19.4
26.8
56.9
22.1
22.9
43.2
9.7
27.8
32.3
24.2
27.3
40.7
28.1
G&M
16.4
34.1
10.5
13.7
32.1
7.7
15.7
43.8
12.3
11.4
30.5
6.7
20.5
24.8
17.9
21.6
32.7
22.3
Happу
22.0
39.4
16.9
1988
38.4
14.2
48.7
18.9
18.1
13.2
24.3
21.3
24.8
25.7
PA-CGCI
28.3
16.5
22.7
25.4
44.7
18.4
25.2
55.1
20.9
21.2
11.5
10.2
26.4
31.4
23.7
27.8
40.1
27.2
DEAN
28.9
47.1
23.0
26.3
46.2
18.2
26.1
58.1
19.4
22.1
41.2
12.9
23.1
32.8
28.9
29.1
40.1
30.3
PramptCCD>
24.5
20.3
27.4
57.4
22.1
23.1
11.4
29.9
26.4
29.9
VB-CGCD
34.2
51.8
26.3
31.7
49.2
23.4
31.6
59.9
26.1
26.3
47.9
15.1
33.2
37.3
29.7
32.3
44.5
31.6
PRISM
49.3
64.9
44.2
44.0
60.9
37.0
36.9
60.0
29.1
33.3
56.5
23.5
40.1
48.9
40.1
36.4
46.1
34.1
4.2
MAIN RESULTS
We compare our method with representative continual discovery baselines, including Grow & Merge (G&M) (Zhang al. 2022), Happy (Ma et al. 2024), PA-CGCD (Kim et al. 2023), DEAN (Park) et al. 2024), PromptCCD (Cendra et al.] 2024). and VB-CGCD (Dai & Chauhan 2025). ) as well re-mmeleentted GCD methods (GCD ( (Vaze et al. 2022), SimGCD (Wen et al. 2023), SPT- Net (Wang et al.) 2024b) and RLCD (Liu et al.) 2025a)). We also note that some recent works, such Hiloo (Wang al. 2024a) and CDAD-Net (Rongali al. 2024), have explored handling
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Table 3: Component-wise ablation Real
Table 4: Comparison of separation strategies on
Painting.
Real + Painting.
Components
Real
Painting
Methods
Real Old
Painting
ICC
SAM
IKT
Old
New
AlI
Old
New
All
New
All
Old
New
54.6
68.7
46.5
28.7
28.1
27.9
origin image
55.0
68.7
47.2
29.6
28.9
28.3
58.1
72.9
49.9
35.0
35,9
32.5
entropy-busee
54.4
69.0
46.7
29.9
29.1
28.6
X
56.9
70.2
52.7
33.2
31.8
35.2
energy- meargy-based
55.8
48.1
30.6
29.5
29.9
60.9
74.1
55.1
39.2
39.0
38.2
PRISM
60.9
74.1
55.1
39.2
39.0
38.2
distribution shifts in GCD. However, since these methods require access to the entire dataset rather than oession-based streams, they cannot be directly applied to CCD, and are therefore not included in our comparison. Table [ and Table 2 present the results on the SSB-C and DomainNet bench- marks, respectively. It can be observed that the challenging OW-CCD setting, existing GCD and CCD approaches struggle to cope with domain shifts, leading to unreliable recognition of known classes and poor discovery of new ones. In contrast, our approach consistently achieves more robust clustering performance, outperforming both prior CCD and GCD methods by clear margin. For instance, on CUB-C, our method surpasses the strongest CCD competitor, VB-CGCD, by 15.1 % in the clean domain and 12.3% in the corrupted domain, highlighting its robustness against both semantic and visual perturbations. On the more demanding DomainNet benchmark, similar gains observed. For instance, in the Real->Painting task, PRISM outperforms VB-CGCD by 3.6% on the source domain (Real) and 6.8% on the target domain (Painting). These results highlight that our approach generalizes effectively new domains while reliably discovering novel categories in continuous streams.
4.3 ANALYSIS
Effectiveness of different components. We conduct comprehensive ablation study to examine the contribution of each component in our framework. As shown in Table the baseline performs poorly, highlighting the severe impact of domain shifts on both known and novel category recog- nition. Incorporating the HCS module to separate known from unknown samples, followed by the SAM module, substantially improves clustering accuracy known categories, confirming the effec- tiveness of sparse assignment matching. Introducing the IKT module further enhances the discovery of novel categories, underscoring the importance of preserving robust category associations under distribution shifts. When all components are integrated, the model achieves the best overall per- formance, demonstrating the benefit of combining these modules for reliable open- continual category discovery.
Comparison with alternative separation modules. To further validate the contribution of the HCS module, we carried out a focused ablation study. We compared with three baselines: (1) an entropy-driven separation scheme (Safaci et al.) (024)), (2) energy- approach (Park et al. 2024). and (3) a simplified variant HCS that relies on raw image features without applying fre- quency decomposition. As reported in Table the proposed module consistently outperforms these alternatives. Its strength lies in exploiting high-frequency information, which preserves more de- tailed structural and semantic patterns, allowing the model more effectively separate unlabeled data. This leads to a more reliable basis for recognizing both previously seen and emerging cate- gories in continual discovery. In addition, Figure Appendix provides qualitative illustration of this effect. The HCS module provides clearer separation between known and unknown groups, demonstrating its ability filter out style-related noise while retaining meaningful semantic rela- tions. These observations collectively indicate that HCS is not only beneficial for sample separation but also crucial for enhancing overall performance in open-world category discovery under distribu- tion shifts.
CONCLUSION
In this work, we take the first step toward tackling the challenging problem of open-world continual category discovery and introduce three key innovations address it. First, high-frequency--divee

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category separation module leverages spectral details to reliably distinguish between known and novel categories. Second, a sparse assignment matching module employs proximal optimal trans- port to assign trustworthy clustering labels to known classes. Third, invariant knowledge trans- fer module enforces semantic association consistency across domains, enabling robust knowledge transfer under distributional shifts. Extensive experiments fectiveness of our framework, demonstrating its ability to consistently recognize known categories and uncover new ones in dynamic, non-stationary data streams.
multiple benchmarks validate the ef-
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A APPENDIX
A.1
MORE RELATED WORK
A.1.1 CATEGORY DISCOVERY
Category discovery aims to transfer knowledge from known classes to identify novel concepts, where unlabeled data may contain unseen categories. Novel Class Discovery (NCD) was first in- troduced to explore how knowledge from labeled classes can be leveraged to discover entirely new ones. Early solutions followed a two-stage strategy. For example, AutoNovel (Han al. 2021) employs self-supervised learning with ranking statistics to transfer knowledge for clustering. Sub- sequently, Fini et al. (2021) proposed unified end-to-end framework optimizing multiple objec- tives simultancously. IIC et al.) 2023a) further model inter-class separability and intra-class consistency to improve robustness. OpenMix (Zhong et al. (02T)) dynamically mixes labeled and unlabeled data to refine pseudo-labels and exploit finer relations among novel classes. While NCD assumes that all unlabeled data belong to novel categories, this assumption limits its practicality. To address more realistic scenarios, Generalized Category Discovery (GCD) was introduced, where the unlabeled pool contains both previously seen and unseen categories. Early GCD methods com- bined supervised contrastive objectives with self-supervised representation learning followed by semi-supervised clustering (Vaze et al.] (2022). Later, SimGCD (Wen et al., (023) introduced a para- metric classifier to improve efficiency and inference speed, establishing strong baseline. Building on these foundations, researchers have proposed a series of more advanced approaches to tackle different challenges in Generalized Category Discovery. For instance, Cao al. (2024) introduced a memory-preserving mechanism to alleviate catastrophic forgetting and maintain knowledge of seen classes during novel category adaptation. Liu et al. (2025b) explored hierarchical space modeling, arguing that Euclidean or spherical spaces are suboptimal for encoding data with hierarchical struc- tures, and instead proposed a hyperbolic embedding space to better capture both seen and unseen categories. To unify the treatment of old and new classes, Ma et al. (2025) developed ProtoGCD, which leverages joint prototypes and dual-level pseudo-labeling to balance the recognition of known and novel categories while also estimating the number of unseen classes. Beyond the single- linlledommin setting, Wang et al.] (2024a) and |Ronaali et al. (2024) extended GCD into cross-domale scenarios, addressing domain shift by aligning representations across source and target domains with special- ized augmentation and adversarial strategies. However, their approaches remain limited to static GCD datasets, whereas our work focuses on tackling domain shift under continuous streaming data, a setting that more faithfully reflects real-world dynamics. In parallel, multimodal extensions have also been explored: Zheng et al. (2024) proposed TexIGCD, a two-phase framework that generates descriptive texts via retrieval and employs cross-modality co-teaching, while Wang et al. (2025) introduced TES, which synthesizes pseudo text embeddings from CLIP to bridge visual and tex- tual modalities. Together, these works significantly improve the balance between known and novel classes, and continually push the performance boundaries of GCD across both generic and fine- grained datasets.
Going further, Continuous Category Discovery (CCD) extends GCD to an incremental setting, where models continually receive new streams of unlabeled data. The key challenge lies in discover- ing new categories while retaining knowledge of past ones. Recent progress in CCD has introduced diverse strategies to alleviate forgetting and improve discovery quality. Zhang et al. (2022) presented the Erow-and-Be Mad-Merge framework, which alternates between a growth phase for enriching feature di- versity via self-super learning and merging phase that stabilizes recognition of previously
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learned classes. Wu et al. (0223) proposed a meta-learning optimization approach that balances dlas-diicrimiinatvv representations for known categories with diverse features for novel discovery. Park et al. (2024) designed DEAN, online method that performs discovery through energy -based guidance and enhances reliability using variance-driven feature augmentation. [eedra et al. (2024) introduced PromptCCD, where Gaussian Mixture Prompting acts as dynamic pool that prevents forgetting and enables adaptive estimation of category numbers. Dai & Chauhan (2025) developed VB-CGCD, which explains forgetting as covariance misalignment and employs variational Bayesian inference with covariance-awe
classification to improve robustness under noisy pseudo-labels.
While these advances move CCD closer to practical continual learning, most methods still rely on the simplifying assumption of a fixed domain within each stage. In reality, streaming data often involve domain variations or shifts, making such assumptions unrealistic and motivating new frameworks that explicitly address multi-domain continual discovery.
A.1.2 DOMAIN ADAPTATION
Domain adaptation seeks to mitigate distribution shifts between labeled source and target domain. A key setting is unsupervised domain adaptation (UDA), which leverages labeled source data and unlabeled target data for model adaptation. UDA methods typically learn
represen- approaches minimize statistical differences between domains via noment-matching techniques (e.g.. correlation alignment (Sun & Saenko, 2016) or Maximum Mean Discrepancy (Long et al. 2015) Tzeng et al., (014)), while adversarial approaches (Saito et al. 2018a Sannaranarayanan et al. 2018) employ domain discriminators to encourage indistinguishable cross-domale features. Recently. Transformer based backbones (Doso- vitskiy et al. 2020) have been explored to enhance feature aligmment through attention mechanisms (Sun et J [222 [u et al.) 2021). However, most UDA methods assume joint access to source and target data, which is impractical under privacy constraints. Source-Free Free Domain Adaptation (SFDA) addresses this by adapting only a source-trained model with unlabeled target data. Chidlovskii et al. (2016) suggested using a small set of prototypes instead of the complete source data to facilitate adaptation, while Liang al. (2019) enhanced target learning by iteratively refining pseudo- labels through self-training. SHOT (Krause et al.] [2100; [Shi & Shal (012)) transfers the source- trained encoder to the target domain by combining information maximization with clustering, keeping the classifier unchanged. To further improve pseudo-label reliability. (Yang et al.) (2021) introduced neighborhood consistency regularization across target samples. Beyond these transductive settings, researchers have also examined Open-Set Domain Adaptation (OSDA), where target data may in- volve categories unseen in the source. OSBP (Saito et al.) (0188) introduced a thresholding strategy to separate unknown samples from the known target subset, while STA (Liu et al. 2019) proposed progressive weighting scheme to gradually disentangle them. More recently, ANNA (Li et al.) 2023b) incorporated causal front-door adjustment and decoupled alignment to mitigate semantic bias and enable more reliable transfer under open-set conditions. Although OSDA broadens the applicability of domain adaptation, it still mainly focuses on recognizing known categories, while overlooking further exploration of the unknown category space.
tations to reduce distribution shifts. Discrepancy-basee
A.2 THEORETICAL PROOF
A.2.1
OPTIMIZATION OF SAM
In this section, we elaborate the optimization procedure for solving the Sparse Assignment Matching (SAM) objective. Let &b and yp denote the dual variables. The SAM problem can then be formulated as:
No Ne
Ct-1
Ne CC-1
2
1 +
max Ls P.p
Via
+
(13)
2
j=l
where Nno denotes the number of known samples, 1--1 the number of known category prototypes,
Ö₁ Cty EY is the transport cost, å, and by are the corresponding marginals. To efficiently optimize Eq. equation [3] we adopt the Block Coordinate Descent (BCD) strategy. The updates of the dual variables are derived by alternatingly fixing one variable and optimizing the other.
(1)
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Update of v. Taking the derivative of Ls with respect to eh and setting it yields:
e
((be))
Σ
(Ō:s - -1:)] aăa
(14)
==1
Update of s.
Similarly, for Py, we have:
Nr
( = 9j - -O:1 (C.j-v)) = =ebj
(15)
Update of 7. With the updated dual variables, the primal transport plan y can be updated. At the 1-th iteration, the optimal 7(++1) is obtained as:
(I+1) max (0, (0 + pj + e7ij
(1)
Cig
7ig
(16)
((++1) Cig - s7og
(1)
ij
After several iterations, the optimal solutions of and yp are obtained, based on which the corre- sponding optimal transport plan y can be subsequently derived.
Table 5: Class counts at each incremental stage for the Corrupted SSB and DomainNet benchmarks. We present cumulative number of categories in both Original and Corrupted settings over four stages.
Stage
CUB-C
Stanford Cars-C
FGVC-Aireraft-C
Domain
Original
Corrupted
Original
Corrupted
Original
Corrupted
Real
Other Domains
140
N/A
130
N/A
70
N/A
225
N/A
160
160
152
152
80
265
265
2
180
180
174
174
90
90
305
305
3
200
200
196
196
100
100
345
345
Table 6: Overview of class partitions in the labeled dataset (D') and unlabeled streams (Dt, D2,
Dj), covering both the known and unknown domains.
Known Domain
Unknown Domaín
Class Range
D
D
D
D
D
D
D
D
1/ E 11 C.
87%
7%
3%
3%
0%
7%
3%
3%
E (0.7 0.8|C]]
0%
70%
20%
10%
0%
70%
20%
10%
V/ E (0.8 0.9[C]]
0%
0%
90%
10%
0%
0%
90%
10%
Vi E (0.9/C), [CI
0%
0%
0%
100%
0%
0%
0%
100%
A.2.2 THEORETICAL INTUITION ON DOMAIN INVARIANCE HIGH--RREQUBNY CUBS
Given an input image (:()) R from domain d E {s, t), we apply the discrete Fourier transform (DFT) F(.) and its inverse F-)(.). A binary mask M E Brex is constructed to separate low- and high-frequency components:
1, if max([u- 11.10- " S Ma $000
min( (H,WW
(17)
otherwise,
and we define
F()) M F(z). (I- F(z),
(18)
where @ denotes element-wise multiplication. The corresponding spatial components are obtained
by
-= d -=
(19)
16

---

Published as a conference paper at ICLR 2026
To analyze the domain dependence of different frequency bands, assume simple additive de- composition:
(d) + p(d)
(20)
where u denotes the domain-shared semantic structure (edges, textures, shapes), and (d()) represents the comain-specific style (illumination, color tone, or imaging pipeline). In the frequency domain, this becomes
F(u))
(21)
Step 1: High-frequency discrepancy is upper-bounded by the high-frequency tail of style. For the high-pass band S2,. (r) selected by (1 -M, we have
(I "
(22)
Since each domain style o(d) is -moooh (m 1) with bounded Sobolev norm || pm S B, then the Fourier energy of its high-frequency tail decays as
(ww) du S Cm p(r)) B²
(23)
which implies
((1-M) @ F(v(d))22 C2122((1) p(r)-(m-11) -(m-1)
(24)
Substituting into Eq. equation
22 obtain
(x -
F² (z (2) S 20 p(r) (m- E(r).
(25)
As the cutoff frequency p(r) increases, E(r) + 0, which means the inter-domain difference in the gebhfrryuency band becomes negligible, and the frequency representation is effectively deter- mined by the shared semanties u.
Step 2: Low-frequency discrepancy is dominated by style. For the low-pass band (4(r), we have ||F F = -
= [Mo(F((")))((
l|
(26)
Since F(b(d) concentrates energy near the origin, the right-hand side non-negligible across do- mains, showing that wwwffreuency spectra encode style and illumination variations.
Step 3: Physical imaging models reinforce this separation. In practice, cross-domain shifts often arise from: (i) multiplicative/addiiii low-frequency fields
x (d) (p) = a (p) (p) + (p))
(27)
where (d) and b(d) are slowly varying and thus mainly perturb the low-frequency spectrum; and (ii) convolution with smooth kernels k(dd,, whose transfer functions k(((() are low-pass, further attenuating style at high frequency. Both mechanisms reduce the figh-frequency contribution of y(d) and thus tighten the bound E(r) above.
Conclusion. Combining the above derivations and the Fourier decay property yields
Therefore, high-frequent components (I - M) O F(x) encode remains significant
[F(*)) ( & ((r)+00,
( ('
(28)
semantic struc- tures, while frequreye components M F(x) capture domain-speciin styles. This theoretical analysis explains why the high-frequency cues extracted (3)(3) are inherently more robust and Bomain-invariant in practice.
A.3 PSEUDOCODE
The pseudocode of PRISM, outlining its main components and training flow, is provided in Algo- rithm [
17

---

Published as a conference paper at ICLR 2026
Algorithm 1: PRISM
Input labeled base set D'; streaming unlabeled sets (D)) model 0 = { : mask ratio SAM proximal strength rank loss weight À
Output: updated model {/.g]
x
Hligh--requeney-Diiyy
Categery Separation (HC3)
Function HCS_Split (Di (e (,r)
for E D do
Compute Fourier spectrum F(x) with mask M;
Extract high-frequency part r";
S(x) v(max, (f(=">.ef 13 [[((**)llle
end for
Fit 2-comp GMM (S(x)) and get (xx)
Dixxo f (x) 2 0.5), Di,u - (2/m((2 < 0.5)
10
return D Di. Sparse Assignment Matching (SAM)
Function SAM.Assign (D), Amos I, {c.-"),,)) ))e);
12
Build cost C. log(g(/ (I))
Initialize (0)
14
15
Solve dual (v, ()) and update y until convergence;
16
yhtm <- arg max, 7ky
17
return (Ut 100
}
18
Invari knowledge Transfer (IKT)
Function IKI -aK_Rankoss (D) (amm )): })
Estimate low-frequency stats from prev. stage;
21
for E Dijum do
22
Generate style-perturbed view F;
23
== f(x). f(2);
Compute PL dists P(P))))
25
Accumulate εKL
26
end for
Lrank return Lrast
divergence
28
29
Affinity Propagation + Online Update
Function AP_Cluster (D) f):
31
Run Affinity Propagation on {f(x));
return novel clusters { Kim
33
Function Online Update e (0, Swo, Swn,, Limut, A1):
34
Le +- cross- -ntss-y on pseudo + novel clusters;
Liaat Lee + CrCki
36
Update model = [J.g]:
return 0 {.. updated prototypes {
38 /+
Main Procedure
39
Initialize = [. and get known-class prototypes {c22 on D
40 for = to to 7' do
Di. Du +- HCs_split(PP, (e r,
[1) ). <- SAM Assign(Di, Lmo. f.g. (,(-)))e)
42
43
Lrank +- IKT Rank (D unnks (ee
{g Kuna t- AP sp_clusterr. (D, f)
44
45
Build pseudo-labeled sets Sum. Sunk:
46
[ct]] + On1 ine Updateeppp Sum, Sunk, Cronk A1)
end for
return updated model 0 [..9)
18

---

Published as a conference paper at ICLR 2026
A.4 DATASETS
To thoroughly evaluate the proposed framework under both domain shift and semantic shift con- ditions, we conduct experiments on two widely used benchmarks: DomainNet (Peng et al. 2019) and SSB-C (Wang et al. 2024a). These datasets encompass diverse visual domains and fine-grained recognition challenges, thereby providing rigorous test of generalization and robustness.
A.4.1 DOMAINNET
DomainNet (Peng et al. 2019) is among the largest benchmarks domain adaptation and general- ization, containing approximately 600,000 images across 345 categories. The dataset spans six het- erogeneous domains with distinct visual styles: Real (photographic images), Clipart (cartoon-style drawings), Sketch (hand-draw) sketches), Painting (artistic renderings such oil and watercolor), Infograph (symbolic infographic-like images), and Quickdraw (doodle-style drawings from Google QuickDraw). The large scale and stylistic diversity introduce strong domain discrepancies, making DomainNet a challenging testbed for algorithms aiming to learn domain-invann invariant yet discriminative representations.
A.4.2 SSB-C
The SSB-C benchmark (Wang et al. 2024a) extends the Semantic Shift Benchmark (SSB) to explic- itly measure robustness under semantic and distributional perturbations. The original SSB is built from three fine-grained datasets: CUB-200-2011 (200 bird species with subtle inter-class variations), Stanford Cars (196 categories covering wide range of brands and models), and FGVC-Aireraft (100 aircraft categories defined by structural differences). SSB-C introduces nine corruption types (e.g., Gaussian noise, frost blur, impulse noise) applied at five severity levels, following the com- mon corruption protocol. This produces a dataset that is nearly 45x larger than the original SSB, offering comprehensive benchmark for evaluating robustness in fine-grained recognition.
A.4.3 EVALUATION PROTOCOL
For each benchmark, a subset of categories is initially designated as labeled known classes to build the first training session. In subsequent sessions, new categories are gradually introduced, simulat- ing the progressive emergence of novel classes. Detailed statistics of category splits are presented in 5 while the proportion of known and unknown samples across unseen domains is summarized in Table These staged splits emulate real-world deployment scenarios which both novel cate- gories and domain shifts arise over time. Methods are evaluated by their ability to simultaneously recognize known classes and discover unknown ones, with particular emphasis on generalization and semantic separability.
Table Clustering results (mean std) on the DomainNet benchmark. The Real domain is treated as the known domain, while each of the other domains serves in turn as the unknown domain. We present the averaged accuracies on /ll/ Old New classes across all stages for both domains.
Table 8: Clustering results (mean + std) on the SSB-C benchmarks. For each dataset, we evaluate on both Original and Corrupted domains, reporting average accuracies over All, Old, and New categories across different stages.
Conyan
Oribial
Compas
Odgeall
Comput
14
1419
150
012
16.0
488
45313
231
342
173
413-14
120m-13 03
21--1
182449
222-3
611-11 100
50
181013
682
615
11
000135
170100
15
001:16
155
55:11
2551.9
mm
43:11
$11114
361.13
441114
3611112
19

---

Published as a conference paper at ICLR 2026
A.5 COMPREHENSIVE CLUSTERING EVALUATION
To assess both robustness and effectiveness, we perform extensive multi-stage clustering studies on the SSB-C and DomainNet benchmarks. The summarized outcomes in Tables 7 and g report the overall performance with corresponding standard deviations (mean + std). showing that our approach consistently outperforms prior baselines in terms of accuracy and stability.
Table Stage-wise clustering performance (%) of different methods on the DomainNet benchmark. Results are reported for all categories (All), previously known categories (Old), and newly discov- ered categories (New) at each incremental stage, along with the overall average.
Mathodc
All
Stage Old
New
All
Stage Old
New
All
StagE 3 Oli
Now
All
Avergg
Ol
New Auai
All
Stage: Old
New
All
Stage Old
New
All
Stags OM
New
All
Avarage Old
New
QCD
$4.4
70.6
47.7
47.8
639
43.1
51.7
67.1
45.4
51.3
67.2
45.4
28.6
28.0
29.3
26.1
25.4
26.8
27.5
26.7
26.3
27.4
26.7
28.1
SimGE1
51.7
44.1
656
61.3
18.4
46.0
63.9
41.4
43.4
61.9
41.3
238
236
36.1
21.4
21.3
22.5
22.6
22.3
25.5
22.6
21.4
21.5
SPTNKx
61.7
50.0
64.7
25.
32.9
23.5
G&MM
50.0
37.
62.2
26.6
27.3
24.9
26.2
25.3
25.5
2622
PV.CODD
58.1
73.6
50.8
53.8
67.3
453
55.3
10.0
48.3
554
70.3
18.1
51.3
33.0
31.5
28.5
29.5
2010
30.2
31.0
30.3
30.1
30.8
30.2
DEAN
58.6
75.1
$1.0
52.9
69.3
65.3
36.5
71.3
43.4
56.0
71.7
47.9
33.9
35.8
32.5
31.7
33.0
50.4
52.8
34.6
31.5
32.8
34.4
31.5
59.1
74.6
53.3
53.9
67.
47.1
56.5
71.1
50.0
56.5
71.2
50.3
32.7
33.4
32.5
30.2
30.5
29.9
31.
32.1
31.2
31.5
31.1
31.2
50.3
$4.9
$4.4
69.
49.5
57.1
11.1
53.8
57.5
52.4
54.9
15.6
31.3
12.4
$1.2
52.4
53.5
12.6
11.4
33.6
52.5
PRISM
55.1
39.0
Red
Starch
35.4
02.4
3966
52.7
06.6
15.81
11.3
7.9
13.2
11.3
912
14.5
10.1
SPTNxx
35.3
50.]
62.5
11.1
6.7
10.4
11.7
9.6
RLCD
53.7
08.0
43.6
68.9
61.8
37.4
51.1
66.6
.0.3
51.2
64.8
40.1
9.5
1333
11.2
7.1
10.5
5.8
12.2
10.0
8.4
12.1
10.0
GIEM
51.8
57.2
46.1
46.3
59.7
38.5
48.7
63.3
42.3
48.9
63.4
42.3
12.3
162
11.5
0.5
14.0
9.4
11.9
15.1
10.5
800
15.1
10.5
IA-CRED
17.8
74.0
48:3
53.1
67.3
45.7
55.0
1044
46.8
11.1
70.7
46.6
13.5
17.5
12.5
11.0
16.6
10:1
12.4
16.1
11.1
12.1
16.1
11.2
DEAN
59.3
74.9
36.9
536
68.1
45.0
57.1
71.5
43.1
56.7
71.5
47.6
14.0
18.2
12.3
11.8
15.4
10.1
12.9
16.8
11.2
12.9
16.
11.2
PronyICCD
50.0
45.
97.4
13.4
12.1
17.7
12.1
733
21.
155
189
20.1
159
Roal Ohickshu
Real
35.3
7.0
4.7
5.5
SimOCD
35.7
13.5
29.5
47.9
23.5
6.3
4.0
5.1
SPTNa
37.9
55.2
27.6
35.00
53.8
24.4
31.4
40.5
22.3
34.8
52.6
24.8
6.1
5.8
7.0
4.5
4.7
5.5
3.7
3.3
4.1
4.9
4.6
5.5
RL133
18.6
57.2
26.2
36.0
61.8
25.9
35.6
51.0
23.0
96.1
54.0
25.7
6.0
5.9
6.5
4.9
488
533
3.5
3.4
4.1
4.8
4.7
5.3
G&MM
37.0
54.0
31.1
33.9
50.1
27.3
31.5
46.5
23.5
54.1
90.2
21.3
4.9
5.2
6.3
3.6
4.1
52
4.1
3.5
4.1
5.2
PA-CECD
09.1
16.
57.4
7.5
52
51
50
6.0
DEAN
7.2
50
PoanCCCD
788
5.9
58
51
VB-COCU
50.1
61.2
38.5
46.1
59.9
18.1
7.4
5.6
4.8
5.6
4.9
6.1
PRISM
56.4
76.5
53,0
53.9
73.
40.1
51.7
71.6
45.5
54.0
74.0
40.2
9.5
7.5
8.5
7.1
6.4
7.3
577
5.3
6.1
7.1
65
7.4
Real
Clyynii
Cliparr
OCD
40.8
59.1
42.4
43.2
63.4
37.5
47.1
65.6
40.1
46.7
65.7
40.
15.7
22.5
11.3
13.2
19:9
5.8
14.6
21.2
10.3
14.5
21.2
10.1
SimtE13
45.5
61.2
36.3
33.4
56-1
30.5
39.6
56.6
33.6
40.2
56.8
35.5
11.5
20.0
0.4
9.1
17:1
1.0
10.5
18.7
8.2
80.3
15.5
8.1
SPTNM
46.2
62.9
36.7
39.7
57.5
35.4
43.3
60.5
35.5
43.1
60.3
35.9
12.8
20.5
10.4
10.4
18.0
7.5
11.5
19.0
4.99
13.6
19.3
5.9
47.7
65.
98.1
14.2
45.1
61.9
22.1
11.3
13.3
19.6
13.6
21.0
20.9
9.6
GLM
10.9
19.2
8.5
13.6
16.5
24.5
12.3
DEAN
57.7
69.3
55.6
12.7
21.4
28.1
16.1
19.1
25.3
26.3
15.0
PraptECD
56.7
746
50.2
31.5
610
43.5
54.1
71.1
46A4
54.1
71.2
46.7
21.0
27.4
15.
18.5
24.5
13.1
19.9
26.1
14.4
1938
26.1
14A4
VB-CG003
58.4
74.1
30.0
52.5
693
44.
55.5
72.1
49.9
55.4
7200
47.5
20.
27.1
15.5
18.4
34.8
12.9
19.6
25.7
14.3
1966
25.8
14.2
PRISM
50.4
748
55.0
55.7
693
47.5
$7.9
12.3
51.1
5010
72.3
$1.3
25.4
31.7
20.5
23.6
29.3
17
3430
30.3
19.0
24.0
30.4
19.1
Rrdi
Infronaph
CKT
43.9
56.7
31.7
00.3
55.3
33.4
16.1
$3.0
.0.1
19.3
55.3
35.4
9.3
11:1
7.6
8.8
9.6
5.6
6.8
8.5
5.1
8.1
9%
6.4
SinGET
16.9
51.6
11.2
.0.3
22.9
10.6
46.6
24.9
51.6
00.2
27.8
9/0
6.6
6.7
2.7
4.0
6.7
7.8
5.1
SPTNk
39.0
20.4
32.5
48.6
7.4
7.1
8.0
G&M
35.3
500
27.6
29.5
46.4
6.6
7.6
92
55
PLCCKD
47.9
54.
6300
38.5
42.5
16.1
10.2
1500
8-4
9.1
12.0
9.0
90
11.8
DEAN
49.3
K8.7
41.9
402
61.3
40.5
43.5
58.9
38.2
46.7
62.3
40.8
10.6
13.9
9.0
9.5
12.5
7.8
$.4
11.1
68
9.5
11.5
7.9
19.7
56.5
43.7
47.1
631
79.9
46.5
59.6
37.0
47.1
63.1
003
10.4
13.5
9.1
9.5
13.3
7.8
7.8
10.9
6.5
9.3
15.3
7.8
VR-411311
51.3
66.0
68.
640
42.
48.4
61.
94.0
43.5
41.9
13.
9.5
9.4
12.3
11.4
8.0
PRISM
109
14.1
In addition, a fine-grained analysis of stage-by-stage performance is provided in Tables 9 and 10 These results include clustering accuracy (%) on All, Old, and New categories across incremental stages, as well as the overall averages. Table focuses on the DomainNet benchmark under different domain shift scenarios, whereas Table I0 presents evaluations C, and CUB-C. Such detailed investigations further highlight the strength of our method in reliably identifying novel categories under both distributional changes and sequential leamnnn settings.
RGVC-Aireraft- Stanford Cars-
20

---

Published as a conference paper at ICLR 2026
Table 10: Stage-wise clustering accuracy (%) of all methods on FGVC-Aireraft-C, Stanford Cars-C, and CUB-C C datasets. We report the accuracy on all classes (All), known classes (Old), and novel classes (New) at each incremental stage, as well as the average.
Mathode
All
Stage Old
New
Sogg: OM
New
All
Stage 3
Oli
Now
All
Aurragg OM
New
All
Sage
Ol
New
All
Nugge Ol
Nex
All
Stagy OM
New
All
Avrrngg
Old
New
RGTC-AincgghC
Carnggol
OCD
30.8
57.0
27.2
34.2
90.3
22.6
26,1
35.5
34.9
27.7
33.6
34.9
30.0
42.7
30.0
21.5
40.3
27.5
26.9
41.4
26.0
15.8
41.4
24.8
SinOCD
26.7
12.5
24.9
22.6
23.2
19.2
25.0
30.1
22.2
25.4
36.1
22.1
26.4
39.3
21.9
24.3
31.0
24.6
25.2
58.0
25.8
25.2
36.1
25.8
20.3
20.
31.3
22.3
PN-CTRED
791
76.4
20.
16.1
11.1
23.7
41.3
78.5
16.5
18.
35.1
33.8
40.1
27.2
DEAN
16.7
16.2
13.0
2510
29-4
76.1
18.5
12.8
25.4
23.1
13.8
38.9
90.2
41.5
11.
18.0
16.1
28.2
28.
48.1
101
26.1
40.1
90.
PoRyCCCD
12.5
37.9
29.9
27.3
31.2
23.2
29.9
34.4
36.1
23.9
34.5
36.4
31,5
442
31.2
29.0
41.5
28.6
30.6
42.9
29.9
9033
42.9
21.
VB-CUCD
36.2
39.4
32.2
30.3
35.1
26.8
35.1
37.4
33.2
31.3
19.7
33.5
45.8
12.9
31.1
43.3
30.3
52.5
44.4
31.7
12.3
44.5
31.6
PRISM
155
Origonal
Carpatted
22.9
31.5
$5.9
33.1
34.4
10.9
11.0
41.3
G&M
18.6
47.6
16.1
15.5
43.7
12.3
13.1
40.1
8.5
15.7
43.8
12.3
1288
31.6
788
13.5
30.5
10.0
20
5.6
11.4
30.5
6.7
PA-CECD
27.9
58.6
23.6
25.1
51.8
31.1
22.6
52.1
18.
352
55.1
20.9
22.4
42.7
11.5
21.3
41.7
10.1
19.
40.2
98
21.2
41.5
10.2
DEAN
26.7
61.5
22.5
26.6
58.1
18.9
23.0
54.7
168
26.1
58.1
19.4
23.2
42.6
14.0
22.1
41.2
129
21.0
39,8
11.5
22.1
41.2
12.9
33.3
Camppdd
SimtEE
18.1
16.3
-3.6
18.9
13.7
SPTNex
30.9
47.8
24.3
24.4
42.4
19.5
25.0
65.4
21.6
27.6
45.2
22.0
26.3
45.4
19.5
23.9
42.9
16.7
25.0
44.3
15.1
25.1
44.2
18.1
RLLTD
31.6
50.0
26.3
26.8
43.8
31.1
79.0
66.6
24.0
28.1
46.8
23.8
25.
06.5
20.5
25.9
46.0
18.2
27.5
45.8
19.4
37.2
45.3
19.8
GAMM
19.3
37.9
14.3
13.
304
6.7
16.2
34.0
10.5
16.4
34.1
10.5
15.1
33.2
3.8
12.3
31.5
13.8
32.1
7.7
13.7
31.1
7.3
IA-CTRCD
31.0
498
25-7
19.8
18.1
46.2
22.9
25.3
12.7
45.9
19.7
34.1
45.4
17.2
23.5
25.4
44.7
184
PompOCCT
VR-C72111
23.4
14.1
$1.9
36.7
10.5
48.0
493
214
PRISM
51.7
07.4
48.0
47.0
03.5
40.5
49.3
64.5
44.1
23.3
44.2
03.2
38.
42.5
59.7
44.0
36.9
44.0
609
37.0
Known domain
Unknown domain
Known
Unknown classes
VB-CGCD
PRISM
VB-CGCD
PRISM
Figure 4: Attention heatmaps from the final VIT layer the CUB-C benchmark. Red indicates the top 10% patches with the highest attention weights across multiple heads. Compared to background patterns, model maintains stronger focus semantic object areas in both known and unknown domains, highlighting resilience to appearance variations.
A.6 ATTENTION MAP VISUALIZATION
To further probe how model performs spatial reasoning, inspect the attention distributions of the last transformer block, focusing on the relationship between the [CLS] token and the indi- vidual patch tokens across different heads. For each sample, we calculate the attention weights and
21

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Published as a conference paper at ICLR 2026
highlight in red the top 10% regions receiving the strongest responses, thereby revealing the areas the model regards as most informative. Figure 4 illustrates representative visualizations from both source and target domains, including examples from seen well novel categories. Regardless of the data setting, our method consistently emphasizes semantically meaningful parts of the ob- ject, rather than being distracted by superficial appearance differences domain-specifer artifacts. This indicates that the learned attention patterns capture object-level semantics in a stable manner. Such consistency in attention allocation underscores the model's ability filter irrelevant back- ground details and concentrate on discriminative structures. By anchoring the focus on task-relevant cues, the representations acquired by our framework generalize better across domains and facilitate reliable discovery of novel categories.
Table 11: Clustering performance of other DA methods.
Real Old
Painting
Method
All
New
All
Old
New
UOL
55.1
69.0
45.6
29.0
29.3
27.6
Mixstyle
53.2
67.1
44.0
26.0
25.2
25.1
cUADAL
55.7
70.1
46.4
29.9
28.8
26.3
ANNA
54.8
68.4
48.7
30.7
28.9
26.8
PRISM
60.9
74.1
55.1
39.2
39.0
38.2
A.7 INTEGRATING CONTEMPORARY DOMAIN ADAPTATION METHODS FOR OW-CCD
To further investigate whether the challenges of OW-CCD can be addressed by existing techniques, we directly applied several contemporary domain adaptation (DA) methods, including Mixstyle (Xu al. 2020). 2020). class-unknown adversarial adaptation (cUADAL) (Jang al. 2022), unknown-oriented learning (UOL) (Liu et al. 2022). and the adjustment- wwweeett-nd-aaginnnane aliggnenmennt approach (ANNA) (Li et al. 2023b). The results, summarized in Table [1 show that these methods bring only limited improve- ments and sometimes even lead to negative transfer, as they focus on distribution alignment but lack the ability robustly discover novel categories. In contrast, our proposed method consistently out- performs these DA baselines across all benchmarks. The results underscore two key insights: (1) addressing OW-CCD requires going beyond simple domain alignment, by explicitly modeling the interplay between known and unknown categories under evolving distributions: and (2) the proposed design provides a more principled solution tailored for OW-CCD, Taken together, these findings val- idate the necessity of customizing algorithms for OW-CCD, rather than relying on direct adaptations of existing DA methods.
Table 12:
Clustering performance on SSB-C benchmarks using DINOv2 as the backbone. Each dataset includes both Original and Corrupted settings, and we report the average All / Old / New accuracy across all stages for both domains.
CUB-C
Stanford Cars-C
RCVC-Alreraft-C
Methods
Original Old
Corrupted Old
Original Old
Commupted Old
Original Old
Corrupted Old
All
New
All
New
New
All
New
All
New
All
New
OCD
41.2
35.6
34,9
$3.9
31.5
65.4
43.1
12.3
53.7
32.3
35.1
39.3
36.1
39.1
49.3
40.4
SimGCD
39.3
52.4
33.6
35.3
51.4
30.5
42.5
63.3
41.0
39.5
50.0
30.9
36.1
37.4
34.1
36.1
44.2
34,7
SPTNet
40.5
52.4
34,3
36.9
51.5
30.6
45.0
64,5
41.4
40.4
51.7
32.1
36.0
36.9
31.3
37.8
45.4
36.4
54.3
53.4
32.3
43.1
33.8
38.2
G&M
28.2
42.6
22.4
25.3
39.3
20.7
36.1
52.8
34.6
30.9
41.0
29.5
31.4
309
28.6
31.8
40.3
31.7
PA-COCD
40.9
53.8
35.2
38.0
53.2
31.1
45.2
65.6
43.1
40.1
51.6
31,3
38.3
40.2
35.0
37.0
45.7
37,3
DEAN
54.5
29.6
67.4
41.7
38.1
PrompiOCD
42.7
55.6
36.0
39.1
54.
33.2
48
67.8
43.7
43.3
53.7
34.1
40.4
42.4
35.9
40.0
48.3
41.0
VB-OGCD
43.1
56.2
38.1
40.7
55.9
35.9
50.5
68.9
45.7
45.4
56.1
35.3
42.3
43.6
37.8
41.8
49,8
42.5
A.8 EMPIRICAL STUDY WITH DINOV2 BACKBONE
To further assess the robustness and effectiveness of our proposed framework, we conduct additional experiments using a stronger pretrained backbone, DINOv2, which has recently demonstrated su- perior representation learning ability in various vision tasks. As shown in Table T2 upgrading the
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Published as a conference paper at ICLR 2026
Table 13: Clustering performance under the setting where multiple domains are treated unknown. Specifically, we construct the unknown set by combining the five domains from DomainNet except for the Real domain, and report clustering accuracy separately for each domain.
Real
Painting Old
Sketch
Quickdraw
Clipurt
Infogruph
Methods
All
Old
New
All
New
All
Old
New
All
Old
New
All
Old
New
All
Old
New
50.9
66.7
44.9
26.9
27.7
9.6
4.7
9.7
6.0
SimGCD
4800
63.5
22.1
22.1
23.2
6,7
10.9
8.9
3.7
8.0
7.8
4.8
SPTNNet
49.4
64.1
42.1
23,7
23.2
24.0
7.6
11.2
9.2
4.5
2.4
3.0
10.3
18.9
8.5
6.9
7.5
5.5
RLCD
50,5
65.1
43.2
25.0
24.9
25.1
8.1
11.9
10.1
4.3
2.2
2.9
10.6
19.2
8.9
7.2
8,0
5.6
G&M
46.7
61.9
40.8
25.9
25.1
25.8
10.5
14.7
10.2
3.9
1.8
2.8
10.1
18.7
8.4
7.1
8.7
5.1
PA-CGCD
55.0
69.9
47.6
29.6
30.4
29.7
11.9
15.7
10.8
4.8
2.5
3.7
15.4
24.1
12.0
11.4
6.8
DEAN
55.5
32.3
31.1
16.4
10.8
5.0
14.6
7.4
PrempiCCD
56.2
70.7
49.8
31,1
31.8
30.8
13.0
17.3
11.7
5.3
4.2
17.6
25.8
14.0
11.8
7.4
VB-CGCD
57.1
71.4
50.3
31.9
32.4
31.7
13.9
18.0
12.4
5.7
3.2
5.1
18.2
26.7
15.1
9.3
12.5
8.2
PRISM
60.4
73.0
34.8
38.8
39.6
37.8
15.8
19.5
14.9
6.9
5.0
4.9
21.9
29.8
19.3
10.9
12.6
9.3
Table 14: Clustering results on the DomainNet benchmark with extended-stage online discovery, We consider Real as the known domain, while each remaining domain is treated as unknown. Scores averaged across all stages (including the 4-stage extension) and reported in terms of All Old / New accuracy for each domain pair.
Metack
Rot Pabniing
Red -Sakk
Bai - Quiddoa
Quicution
Red Cliprt
Clgun
Rell Iskeraa
Exoggrahh
21:
023
282
652
357
333
312F
T012
SP1Ne1
06.1
43.0
26.3
235
348
383
121
92
543
123
244
434
362
190
8.4
15.4
5039
26.5
135
9.3
8:3
B1CD
819
669
03.3
230
343
133
910
033
183
84
1:3
183
10.
111
349
34
31
62
435
6-2
367
123
192
8.-
553
51.2
269
72
9.5
6.3
04.
381
48
118
987
867
221
39
DEAN
713
616
88.
345
118
911
126
483
114
430
691
317
56
113
483
363
567
46.
828
883
131
186
7.4
361
719
126
960
687
111
11-
441
613
163
67
70
941
635
194
255
160
487
626
97
176
83
backbone to DINOv2 consistently boosts the performance of all compared algorithms across differ- ent benchmarks. This confirms that stronger feature extractors can provide more transferable and discriminative representations for the OW-CCD tasks.
More importantly, under this enhanced backbone setting, our proposed method still achieves the best overall performance and maintains clear margin over state-of-the-art baselines. This demonstrates that the improvements brought by our framework are orthogonal to backbone advances, and our method continues to deliver substantial gains even when combined with powerful feature extractors. These results highlight the scalability and practical value of our framework when deployed with next-generation backbones such as DINOv2.
A.9 EVALUATION UNDER MULTIPLE UNKNOWN DOMAINS
To further assess the robustness and practicality of our framework in more complex real-world sce- narios, we conduct an additional experiment under a mixed-domain setting. On the DomainNet benchmark, we treat the Real domain as the known domain and merge all the remaining domains (clipart, Pa nt ing, Sketch, and In fograap)) into single unknown domain. Compared with single-domain shifts, this mixed-domaie setting introduces much greater diversity in both visual styles and semantic structures, making continual category discovery significantly more challenging.
As shown in Table 13 our method consistently outperforms state-of-the-art baselines across all met- rics. In particular, we observe notable improvements on novel category discovery. indicating that the proposed approach remains effective when the unlabeled data come from multiple hetero- geneous domains. These results suggest that our framework generalizes well to realistic scenarios where unlabeled streams are inherently multi-sourced and non-stationary.
A. EVALUATION UNDER MORE-STACE SETTING
In our main evaluation, we design the online discovery task under a three- three-phase setting to verify the effectiveness of the proposed framework. To further examine its robustness and scalability under
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(b)
(c)
Figure 5: Comparison of different separation strategies.
more demanding conditions, we additionally explore a four-phase scenario, where novel categories emerge in slower and more fragmented manner.
As shown in Table 14 our approach consistently surpasses competitive baselines across all evalua- tion metrics and category partitions (AlI, Old, New). These results highlight the framework's ability to cope with increasingly incremental category arrivals, confirming its adaptability to dynamic and extended discovery processes.
A.11 PARAMETER SENSITIVITY ANALYSIS
We further investigate how our method behaves under variations of the loss balancing coefficient A1 with results shown in Figure 6
(a) (b) and (g)-(h). Experiments are conducted on both DomainNet and CUB-C. For DomainNet, the Real domain treated as the known domain and the Painting do- main as the unknown domain. For CUB-C, we follow the same protocol by designating the Original domain as the known domain and the Corrupted domain as the unknown domain. By adjusting Λ1 measure clustering accuracy all, old, and novel categories within both domains. A clear performance drop is observed when this coefficient is set to zero, highlighting the indispensability of the IKT module. This component aligns listwise ranking distributions between unknown sam- ples and known prototypes before and after spectral perturbation, thereby mitigating spurious style effects and retaining transferable semantic knowledge. More importantly, across wide range of À values, the accuracy remains consistently high, indicating that our approach is largely insensitive to this parameter and thus robust against hyperparameter tuning.
The mask ratio regulates the binary mask M used to split an image into low- and high- high-frequency parts, controlling their relative contribution. In our design, figh-freueecy signals guide the separa- tion of known and novel categories, while low-freog wwwffreuuecyy content is perturbed within the IKT mod- ule encourage transferability. As presented in Figure (c)-(d and (i)-() performance peaks at 0.3, which we adopt as the default setting in all experiments unless stated otherwise. Increasing allows the model to exploit richer semantic cues from high-frequency components and enhances robustness of the IKT module by perturbing a broader spectrum of low- low-frequency information. Nev- ertheless, when r becomes excessively large, the model suffers from limited geehffry cues for separation and over-distortion of semantics within the IKT module, which together harm discrimi- native ability.
We further conduct parameter sensitivity analysis the proximal strength coefficient in the SAM module. Specifically, we perform experiments on the Real + Painting domain adaptation sce- nario, varying E within the range {0.0, 0.01, 0.05, 0.1, 0.5, 1, 10). As shown in Figure 6 (e)-(f) and (k)-(), the performance of our model remains stable under moderate changes of e. However, when e becomes excessively large, the proximal regularization term dominates over the sparse assignment matching, which may lead to performance degradation. Therefore, we empirically set e = 0.5 in all experiments.
A. COMPUTATIONAL COMPLEXITY ANALYSIS
We profile the computational overhead of each component on an RTX4090 with batch size 128 and input resolution 224 x 224. HCS performs one forward DFT/IDFT pair per image (via torch. fft), applies a binary frequency-plan mask, runs a simila-sym sean against previous-stage prototypes, and fits ID GMM on scalar scores; the cost is dominated by FFTs, with masking/cosine/GMM. negligible. For the known- like subset, SAM solves a proximal OT subprob- lem with few lightweight dual updates over (v), (p) and closed-form refresh of y; computing the
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Parding
75
Clustring Accuracy
Clustaring Accuracy
38
Olsseeiig Accurasy
63
37-
ALL
ALL Old
ALL
55
Old
New
New
New
Balance coefficiant
Balance confficient
Mask ratio
(b)
(c)
Clustering Accuracy
40
Cluszerino Accuracy
Clustering Accuracy
35
30
ALL
80
ALL
Old
Old
New
0.01
0.01
Mask ratio
Proximal strength
Proximal strength e
(d)
Drignel
Comptted
Ongiral
65
60
Clustering Accurecy
Clustering Accuracy
Clusteriig Accuracy
60
-5
55
30
50-
16
88
Old
Old
Old New
30
0.0
coefficiant A
Belance coefficient A
Mask ratio
(h)
Comgree
Drignal
Comupted
Clustering Accuracy
Clustering Accuracy
Clustering Accuracy
10
55-
40
30
ALL
ALL
Old
Old
Old
New
New
Mask ratio
Proximal strength
Proximal strongth
(k)
Figure 6: Sensitivity analysis of key hyperparameters.
cost matrix and sparse projection is minor compared to FFT work. For the unknown-like subset, IKT reuses low-frequency statisties from the previous stage to sample perturbed styles, reconstructs low-frequency spectra and fuses them with the original high-frequent part before an inverse DFT, then compares two feature views (original vs. style-transferred) to all prototypes to build Plackett- Luce listwise distributions and KL divergence; the extra compute is mainly the single IFFT per unknown sample, with ranking terms lightweight. Overall, the framework adds approximately 5.2 GFLOPs and increases per-iteration time by ~0.65 and thanks to GPU-accelerated FFTs, efficient proximal OT updates, and amortized clustering, the overhead remains manageable and scales well to large open-world streams.
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Table 15: Stage-wise clustering accuracy (%) of all methods on the CUB-C dataset under the dy- namic Nomain-incrementee setting. At each stage, new domains are progressively introduced (Stage I: Gaussian, Shot, Impulse Noise; Stage 2: Zoom Blur, Snow, Frost; Stage Fog, Speckle, Spat- ter). We report the accuracy on all classes (All), known classes (Old), and novel classes (New) at each stage, as well as the average across stages.
Mathods
All
Singe I
Old
New
All
Stage Dul
New
All
Stage Old
Nex
AWTrigS Old
New
Ali
Soge Old
New
Stage Old
New
All
Saag Old
Nox
Anerage
Old
New
OCID
51.1
65.4
34.1
38,6
66.2
22.1
34.9
43.3
30.1
25.2
05.3
22.1
23.8
43.0
17.3
36,6
45.0
30,0
25.5
44.5
19.0
25.3
66.5
18.7
SinOCD
28.2
45.4
21.3
24.8
43.0
20.0
22.5
48.9
17.2
25.2
43.0
194
250
39.8
15.1
23.3
42.4
13.3
22.0
40.9
15.4
22.9
41.0
16.4
SPTTN
46.0
13.3
63.9
41.2
30.7
31.4
41.3
25.1
48.1
18.5
236
430
25.7
42.8
16.8
RLCD
23.3
19.3
2500
18.0
PL-CODD
294
48.1
23.9
26.9
21.6
214
22.5
18.5
242
12.0
BEAN
29.
49.9
34.5
28.
65.7
21.2
45.
313
353
36.4
18.1
248
6.8
169
31.1
49.8
264
26.7
66.5
22.8
16.3
43.
20.1
26.
466
25.1
24,6
43.2
17.4
27.3
46.3
20.4
262
446
13.8
36.0
66.7
189
VI-CORD
56.7
50.3
31.3
51.9
18.5
25.6
19.3
46.1
23.4
310
05.4
75.0
76.5
45.4
19.5
75.6
48.4
22.4
25.4
66.6
31.1
78.3
06.8
71.0
PRISM
511
66.7
47.
489
64.4
43.9
47.8
62.
48.5
45.0
645
448
41.9
59.1
35.1
45.1
61.9
38.2
436
602
36.5
43.5
00.4
36.6
A.13 EXPERIMENTS ON DYNAMIC DOMAIN-INCREMENT INCREMENTAL SETTING
To further evaluate the performance of our proposed algorithm in dynamic domains, we conducted an additional domain-incremental experiment. Specifically, we trained on the CUB-C dataset over three stages, where new domains were progressively introduced at each stage. In Stage 1, we in- troduced three types of perturbations: Gaussian Noise, Shot Noise, and Impulse Noise. In Stage 2, further incorporated Zoom Blur, Snow, and Frost. In Stage 3, additional perturbations includ- ing Fog, Speckle, and Spatter were introduced. In this way, we simulated a realistic dynamic and non-stationary data stream scenario, where each stage may involve domains unseen in the previous stage. As shown in Table [1]] we observed that under this more challenging dynamic-domin set- ting, our model still achieved significant performance improvements and substantially outperformed other competing methods, further demonstrating the robustness and effectiveness of the proposed approach.
A.14 VISUALIZATION HCS SEPARATION ACROSS FINE-GRAINED DATASETS
We further visualize the separation behavior of HCS on several fine-grained datasets, illustrated in Fig. 7 Across most datasets, the separation scores of known and unknown samples show a certain degree of discrepancy, indicating that HCS reliably distinguish the two groups using high- high-frequency information. Even on challenging fine-grained benchmarks
as Stanford Cars- C-where categories share strong visual resemblance, the score distributions still exhibit an approx- imate bimodal pattern, demonstrating that meaningful separation can be achieved.
These observations highlight two important facts. First, high-frequeney cues consistently provide better separation than raw images, as the removal of low-ffequencc domain biases makes the remain- ing semantic differences more distinguishable. Second, although the separability on fine-grained datasets is naturally reduced due to subtle inter-class variations, high-frequency decomposition still improves the separation of known and unknown samples.
Overall, the results in Fig. 7 confirm that high-frequency cues offer more reliable and robust sep- aration than original images across diverse datasets, including challenging fine-grained scenarios.
A.15 BAD CASE ANALYSIS
Although the proposed HCS module provides robust separation between known and unknown sam- ples across diverse datasets and corruption types, certain challenging scenarios may still lead to reduced performance. A representative example arises under structural blurring- such zoom which directly suppresses the semantic high-frequency cues that HCS relies on.
Structural blur smears object boundaries, attenuates fine textures, and destroys edge sharpness, thereby weakening the discriminative high-frequency structures essential for our separation mecha- nism. As illustrated in Appendix Fig. the extracted high-frequency representations become less
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50
Unknown Category
50
Unknown Category
Number Samples
40-
Known Category
Number of Samples
Known Category
30
20
20
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
500
(a) CUB-C((Oigin)
(b) CUB-C(HCS)
Unknown Category
50-
Linknown Category
Number of Samples
Known Category
Number Samples
40
Category
30
30
20
20
10
10
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
5(x)
(c) FGVC-AirerafI-C (Origi)))
(d) FGVC-Airerafi-C (HCS)
50
Unknown Category
Unknown Category
Number Samples
Known Category
Number of Samples
50
Category
40
40
30-
30
20
20
10
10
0.0
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0.4
0.6
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1.0
0.0
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1.0
500
5(x)
(e) Stanford Cars-C (Origin)
(I) Stanford Cars-C (HCS)
Figure 7: Comparison of separation performance across multiple fine-graine tne-grained datasets.
informative in these cases, causing the separation scores of known and unknown samples to move closer.
Nevertheless, even under such challenging corruptions, HCS consistently outperforms using raw images. This is because the frequency biases, and the preserved semantic structures- althouugh partially begraded-remanee more discriminative than full-spectrum representations. The resulting separation curve demonstrates that the degradation introduced by structural blur affects both HCS and raw images, but the impact is notably smaller for HCS.
decomposition still removes domain-specific low-
Overall, these bad cases highlight an inherent limitation: when corruptions significantly destroy semantic fighffreyeeccy content, the separability achievable by HCS naturally decreases. This observation suggests promising future cirrction-iitegree structure- preseerripee or deblurring techniques to further enhance the robustness of high-frequency--bee based separation under severe im- age degradations.
A. COMPARISON WITH EXISTING DOMAIN-SHIFT GCD METHODS
Although prior domain-shift GCD approaches such as HiLo (Wang et al. 2024a) and CDAD- Net (Rongali et al. 2024) cannot be directly adapted to the same domain-soee shift ft CCD setting con- sidered in our work, framework still be applied the standard cross-domain GCD scenario
27

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Published as a conference paper at ICLR 2026
Unknown Category Category
Unknown Category
Category
Number of Samples
30
Number of Samples
30
20
20
10
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
(a) Zoom blur (Origin)
(b) Zoom blur (HCS)
Unknown Category Calegory
Unknown Category
Known Category
Number of Samples
Number of Samples
30-
30
20-
20
10
10
0.0
0.2
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1.0
(c) Frosted blur (Origin)
(d) Frosted blur (HCS)
Figure 8: Illustration of a challenging case, where structural blurring suppresses semantic high-
frequency cues and weakens separation, yet HCS still outperforms raw-image representations.
Table 16: Clustering results of various methods the DomainNet benchmark. Each experiment uses Real as the source domain, with one Painting, Sketch, Quickdraw, Clipart, or Infograph serving as the target. Clustering accuracy is reported for both domains.
Resl
Rail OM
Shank
Red
Quidulinn
Clus
AB
New
Al
Nee
Al
Yos
A3
CA
Nev
All
Nap
Al OLI
Al
GM
Nov
Odl
New
All
New
All
OL
710
303
36.
615
IT3
10.
15.
453
307
118
33
313
36.3
for fair comparison. Following the experimental protocol of HiLo, we evaluate our method along- side HiLo and CDAD-Net under the cross-domain GCD setting (note that this is not the proposed OW-CCD scemario). As shown the results, our approach outperforms both HiLo and CDAD-Net in most cases, demonstrating that the proposed method is flexible and effective even when applied to conventional cross-domain GCD tasks.
Clustering results of various methods on the SSB-C benchmark. For each dataset (CUB, Scars, and FGVC). the clean set serves as the source domain, while its corrupted counterpart is treated as the target domain. Clustering accuracy reported for both domains.
Table 17:
CUB-C
Scars-C
PGVC-C
Methods
Original Old
Comupted
Original Old
Commupted Old
Original
Cormetted
All
New
All
Old
New
All
Now
All
New
All
Old
New
All
Old
New
CDAD-Net
40.4
38.9
39.3
37.7
39.1
34,2
32.1
32.2
28.8
21.4
35.5
31,2
27.8
29.6
25.6
Hilo
56.8
54.0
60.3
52.0
53.6
50.5
39.5
44.8
37.0
35.6
42.9
28.4
44.2
50.6
47.
31.2
29.0
33.4
PRISM
60.1
58.7
63.1
56.2
55.1
54.9
44.0
47.4
40.6
40.1
43.5
34.5
47.9
55.1
51.8
357
31.8
39.1
A.17
SOCIETAL IMPACT AND FUTURE DIRECTIONS
We study Open- World Continual Category Discovery (OW-CCD), which reflects the reality of dy- namic data streams where category distributions are non-stationary and new classes emerge over time. This research has broad societal implications, it equips Al systems adaptively recog-
SprndWorld
28

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Published as a conference paper at ICLR 2026
nize evolving concepts in real-world scenarios such medical diagnostics, ecological monitoring, and social media moderation, thereby enhancing their reliability and faimess in open environments. However, limitations remain: (1) the open world introduces vast distributional shifts and complex dynamics, where current models still struggle to maintain stable performance; and (2) most existing work relies single-modality data, whereas extending to multi-modal OW-CCD is crucial fully exploit diverse real-world signals (e.g., combining image, text, and sensor data) for robust knowl- edge discovery. These challenges highlight promising future directions, motivating research into more resilient algorithms and multi- learning frameworks for open- open-world continual discovery.
A.18 ETHICS STATEMENT
This research does not involve human participants, animal subjects, or the use sensitive personal data, nor does it present any potentially harmful applications. All experiments conducted publicly available benchmark datasets that are properly licensed for academic use. The authors are committed adhering ethical research standards and to promoting fairness, transparency, and responsible development of Al technologies.
A.19 USE OF LLMS
During the preparation of this manuscript, we made limited use of publicly available large language models (LLMs) solely to assist with English writing. All technical content, including the formulation of ideas, design methodologies, implementation experiments, and interpretation results, was entirely conceived and written by the authors without LLM involvement. The role of LLMs was strictly confined to stylistic and linguistic improvements, comparable to grammar- or spell-checking software. No novel research insights, data, analyses generated by LLMs, and all scientific claims and results presented in this work remain the sole responsibility of the authors.
A. REPRODUCIBILLITY
To ensure reproducibility, we provide a comprehensive description of the model design in Section and detailed experimental settings in Section Furthermore, we include the full pseudocode of the proposed PRISM framework in Appendix A.3, clearly outlining main components and training flow. These details collectively enable faithful reimplementation and verification of our results.
29


Reviews:
[{"review_id": "LxuOkkiBTZ", "reviewer": "Reviewer_9cpT", "summary": "The paper proposes a new setting called Open-World Continual Category Discovery (OW-CCD), arguing that existing CCD works usually assume a single domain, which is unrealistic for real data streams. To address this, the authors introduce PRISM, a three-part framework:\n\n1. High-Frequency-Driven Category Separation (HCS): use Fourier-domain decomposition to keep high-frequency components and compute a density score over high-frequency features to separate “known-like” vs. “unknown-like” samples. \n2. Sparse Assignment Matching (SAM): for samples considered “known-like”, perform proximal-regularized optimal transport to assign them to known-class prototypes, making the cluster assignment sparser and clearer.\n3. Invariant Knowledge Transfer (IKT): for unknown samples, perform low-frequency perturbation and enforce ranking-level consistency to known prototypes using a Plackett–Luce distribution, so that semantic relations to known classes are invariant across domains. \n\nExperiments on SSB-C and DomainNet show consistent gains over recent CCD baselines; ablations show that each of HCS / SAM / IKT contributes and all three together give the best numbers.", "questions": "1. **Failure cases of HCS.** On which SSB-C corruption types does HCS fail to produce a clearly bimodal similarity distribution? Showing 1–2 negative examples in the appendix would make the claim “HCS yields separable scores” more convincing.\n\n2. **Session/domain order.** On DomainNet, do you randomize the order of arriving domains, or do you always use the same source (e.g., Real) and the same progression of target domains? If the order is fixed, to what extent does PRISM rely on this order (i.e., does it overfit to an “easy → hard” schedule)? Have you tried choosing a non-Real source domain?", "limitations": "", "rating": 2, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. Plausible problem setting: the paper targets CCD under domain shift, which is a realistic gap in current CCD/GCD work.\n\n2. Good empirical performance: shows strong results on multiple benchmarks.\n\n3. Readable: pipeline (HCS → SAM → IKT) is clearly described and easy to follow.", "weaknesses": "1. **Weak and insufficiently argued link to continual learning.**\n   The paper claims to tackle an open-world, continual setting, but the method itself is almost entirely tailored to domain shift. In practice, the pipeline treats the problem as “domain-shift GCD + CCD,” without discussing what actually becomes new or harder once we make it continual (e.g., catastrophic forgetting, order sensitivity, memory constraints). All three modules are clearly shift-oriented — HCS uses frequency cues to be domain-robust, SAM does cross-domain pseudo-labeling, and IKT enforces cross-domain relation consistency — yet none of them is a CL-specific mechanism. As a result, there is a visible gap between the problem the paper advertises (open-world *continual* category discovery) and the solution it delivers (a per-session, domain-shift-aware GCD pipeline). This naturally raises the question: why isn’t this simply framed as “domain-shift GCD” [1,2]? To make the claim convincing, the paper should spell out (i) what challenge is *unique* to their OW-CCD formulation and cannot be handled by combining existing domain-GCD [1, 2] and CCD methods, and (ii) how PRISM actually uses the *continual* nature of the stream, instead of just re-running the same 3-stage block on every session.\n\n2. **Unclear tractability of IKT.**\n   In IKT, the paper models the distribution over **all** permutations of the known-class prototypes using a Plackett–Luce distribution. This is conceptually reasonable, but as the number of known classes grows (e.g., C > 100), enumerating or even implicitly handling all C! permutations becomes infeasible. The paper does not explain how this is solved in the actual implementation. Without this detail, it is hard to judge whether IKT is an actually runnable module or mainly a theoretical description.\n\n3. **Comparisons do not fully match the stated goal.**\n   Since the method is explicitly designed to handle domain shift, it should also be compared to domain-shift GCD methods [1,2]. At minimum, the authors could (a) adapt HiLo [1] or CDAD-Net [2] to the same domain-shift + CCD, or (b) evaluate PRISM directly in a domain-GCD setup to demonstrate that it handles shift better than prior work [1, 2]. At present, most baselines are standard CCD methods that were *not* written for domain shift, so the performance gaps are less conclusive — the method may simply be capitalizing on a challenge (domain shift) that the competing methods were never tuned for.\n\n[1] Hilo: A learning framework for generalized category discovery robust to domain shifts.\n\n[2] Cdad-net: Bridging domain gaps in generalized category discovery.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "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": "", "extra_scores": {}}, {"review_id": "cKV0WmtB24", "reviewer": "Reviewer_PEuj", "summary": "This paper introduces a new category discovery task: Open-World Continual Category Discovery (OW-CCD), which challenges the single-domain data assumption in the previous CCD task.", "questions": "See weaknesses.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.\tNew problem setting:   This paper addresses a realistic open-world streaming setting (OW-CCD) that extends and generalizes prior continual and generalized category discovery work, directly tackling the limitations of stationary-domain assumptions in earlier CCD literature.\n2.\tThe proposed method is interesting, especially since using the frequency domain to provide auxiliary supervision signals is reasonable.\n3.\tThe proposed method achieved good results on the OW-CCD.", "weaknesses": "1.\tI agree that introducing frequency domain analysis into the GCD task is an interesting and intuitively plausible approach. However, the paper's core claim that high-frequency components contain more domain-invariant category discriminative information lacks sufficient experimental support. The current Fig. 1 serves more as an idea demonstration than a rigorous quantitative argument. To enhance persuasiveness, the authors should provide more direct evidence, such as comparing the separation levels of known and unknown classes in high- and low-frequency feature spaces via t-SNE visualization, or quantifying the accuracy advantage of high- and low-frequency features in classifying new versus old categories.\n2.\tThe proposed method introduces a non-trivial number of hyperparameters, among which the mask ratio r is particularly critical and appears to be highly sensitive.  \n3.\tI don't entirely agree with the motivation behind the IKT section. Its optimization principle requires that, regardless of style variations, the similarity ranking between unknown samples and known classes remains consistent—essentially treating known classes as anchors for discovering new ones. However, whether this approach genuinely promotes effective discovery of new classes is debatable. Moreover, in current GCD/CCD research, known classes on some datasets exhibit low accuracy. I think this constraint may limit the representational space for new classes.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "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": "", "extra_scores": {}}, {"review_id": "kbEWSBUzl5", "reviewer": "Reviewer_h5nf", "summary": "This paper introduces the Open-World CCD setting and proposes PRISM, a divide-and-conquer framework with three components: (i) High-Frequency-driven Category Separation (HCS) to split known vs. unknown using spectral cues and a GMM on a density score; (ii) Sparse Assignment Matching (SAM) for proximal-OT based pseudo-labeling of known classes; and (iii) Invariant Knowledge Transfer (IKT) that aligns Plackett–Luce ranking distributions across styles to stabilize transfer to novel classes. Extensive experiments on SSB-C and DomainNet show performance improvement.", "questions": "- Does HCS remain reliable when S(x) is not clearly bimodal (e.g., high domain overlap)? Any fallback beyond the 0.5 posterior cut?\n- How often are prototypes updated across sessions, and how sensitive are results to stale prototypes feeding HCS/IKT?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "- Overall, I appreciate the clear problem formulation. The proposed OWCCD is well motivated and distinct from the static GCD and CCD assumptions under single domain settings.\n- The proposed method seems reasonable. HCS uses DFT high-frequency components with a 2-component GMM for known/unknown separation. SAM applies a proximal ℓ2 OT for sparser. IKT models listwise relations via PL and enforces consistency between original and style-perturbed rankings.\n- The paper is well organized and easy to follow.", "weaknesses": "- The paper requires substantial revisions before acceptance. The font sizes in Tables 1 and 2 should be unified, as the current versions are difficult to read. Figure 1 also has fonts that are too small. The derivation of Equation 3 needs to be clarified, particularly the relationships among F^{l}, F^{h}, and F^{-1}. In addition, the introduction should be improved to enhance readability.\n- The method lacks ablations on key design choices, maybe could include: (i) removing frequency decomposition to test spectral necessity, (ii) replacing PL with pairwise or listwise surrogates and varying \\lambda_{IKT}, and (iii) adjusting OT regularization ε and SAM steps to analyze the accuracy–sparsity–speed trade-off.\n- Provide a small theory/intuition section on why high-frequency cues are more domain-invariant in practice (beyond Fig. 1).\n- Although the method is prototype-based, it does not compare with recent prototype-based approaches, such as [1].\n\n[1] Happy: A debiased learning framework for continual generalized category discovery. NIPS 2024", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "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": "", "extra_scores": {}}, {"review_id": "wHUy91rHdw", "reviewer": "Reviewer_qfwv", "summary": "#### **Summary**: This paper studies Open-World Continual Category Discovery (OW-CCD) where unlabeled streams mix known and novel classes under domain shift across sessions, and proposes PRISM, a progressive, divide-and-conquer framework. PRISM first separates known vs. unknown via a high-frequency image decomposition and a 2-component GMM over prototype similarity scores (HCS). It then assigns sparse, reliable pseudo-labels to known-class samples using proximal optimal transport (SAM). For the unknown pool, it enforces domain-invariant category relations by aligning Plackett-Luce ranking distributions of similarities across style-perturbed views (IKT), and clusters novel classes with Affinity Propagation without a class-count prior. On SSB-C and DomainNet, PRISM reports consistent gains over strong CCD/GCD baselines (e.g., +15.1% / +12.3% vs. VB-CGCD on CUB-C original/corrupted), with ablations supporting each module’s contribution, tested with both DINOv1 & DINOv2.", "questions": "- #### **Q1. Bimodality & fallback:** How consistently does S(x) exhibit a bimodal pattern across sessions/datasets, and is there a fallback (e.g., energy/entropy split) when EM finds overlapping components?\n\n- #### **Q2.** Results include synthetic corruptions and cross-style domains; how does PRISM fare under spatial distortions (large occlusions, extreme down-sampling) that specifically degrade high-frequency cues presumed to be invariant?\n\n- #### **Q3.** Why Affinity Propagation over alternatives (e.g., density-based methods) for unknowns? Any observed failure modes (e.g., over-fragmentation) and mitigations?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- #### **S1. Well-posed, timely setting.:** The paper clearly formulates OW-CCD and explains why naive domain alignment can harm discovery (negative transfer), motivating the split-then-learn design.\n\n- #### **S2. Compelling empirical evidence:** Strong improvements on SSB-C and DomainNet with multi-seed means ± std and component ablations; HCS beats entropy/energy separations.\n\n- #### **S3. Clear technical details:** HCS→SAM→IKT is technically concrete (DFT mask + 2-GMM; proximal OT; PL-ranking KL) and presented with end-to-end pseudocode, aiding reproducibility.\n\n- #### **S4.** Clear presentation, make it easy to understand the overal paper claim.", "weaknesses": "- #### **W1. Concerns on HCS (high-frequency separation) and a brittle bimodality assumption:**  PRISM’s HCS assumes that high-frequency components carry domain-invariant “global semantics”, then splits a stream into known/unknown with a 2-component GMM on a cosine-similarity–based score S(x) and a fixed 0.5 decision rule. The concern is due to the assumption that high frequencies are more domain-invariant/global than low frequencies is non-standard and is supported only by a few visuals and an empirical “often bimodal” observation of S(x). There’s no stress test for cases where the density is not bimodal (e.g., overlapping or multimodal mixtures), or where frequency energy shifts with style/texture corruptions. The split hinges on the mask ratio r and the 2-GMM fit; failure modes (bad EM fits, non-bimodal spectra, class-imbalance drift) aren’t characterized. The paper introduces r and report sensitivity plots, but the text doesn’t establish robustness across tasks/backbones/shifts—only that varied r, ε in Appendix Fig. 6.\n\n- #### **W2. Scalability gap around clustering (Affinity Propagation) in an open-world streaming setting:** Novel-class discovery uses Affinity Propagation (AP) each session on the unknown subset embeddings. This concerns is due to AP is O(n^2) in similarities and memory; in a streaming, open-world scenario this is a bottleneck. The appendix’s compute profile discusses FFTs/SAM/IKT and quotes “5.2 GFLOPs” and “0.65 s” per iteration, claiming the overhead “scales well”, but it doesn’t analyze AP’s time/memory cost or performance under large, long streams. What to show: either (i) replace AP with a streaming/mini-batchable clustering method (e.g., online k-means, DP-means, coresets), or (ii) add a clear AP complexity plus wall-clock and memory study at increasing stream sizes (curves), and (iii) demonstrate stability when unknowns accumulate over many stages.\n\n- #### **W3. Positioning/novelty risk:** OW-CCD may read as “CCD plus domain shift” (an incremental protocol tweak). The paper presents OW-CCD as the first step beyond single-domain-per-stage CCD, arguing prior CCD/GCD assume a stationary domain per stage, but the setting itself can also be interpreted as a composed variant—continual discovery combined with domain shift—rather than a fundamentally new learning paradigm. The paper would benefit from a crisper separation from prior “domain-varying” or “corruption-varying” CCD/GCD works and from OSDA/SFDA lines  or domain-class incremental learning [a] adapted to novelty discovery (too much of this currently sits in the appendix).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "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": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)