Noise-Aware Generalization: Robustness to In-Domain Noise and Out-of-Domain Generalization
Authors: Siqi Wang
Aoming Liu
Bryan A. Plummer
Venue: ICLR 2026 Poster (2026)
Keywords: label noise
domain generalization
noise-robust generalization
Primary area: transfer learning, meta learning, and lifelong learning

Abstract: Methods addressing Learning with Noisy Labels (LNL) and multi-source Domain Generalization (DG) use training techniques to improve downstream task performance in the presence of label noise or domain shifts, respectively. Prior work often explores these tasks in isolation, and the limited work that does investigate their intersection, which we refer to as Noise-Aware Generalization (NAG), only benchmarks existing methods without also proposing an approach to reduce its effect. We find that this is likely due, in part, to the new challenges that arise when exploring NAG, which does not appear in LNL or DG alone. For example, we show that the effectiveness of DG methods is compromised in the presence of label noise, making them largely ineffective. Similarly, LNL methods often overfit to easy-to-learn domains as they confuse domain shifts for label noise. Instead, we propose Domain Labels for Noise Detection (DL4ND), the first direct method developed for NAG which uses our observation that noisy samples that may appear indistinguishable within a single domain often show greater variation when compared across domains. We find DL4ND outperforms DG and LNL methods, including their combinations, even when simplifying the NAG challenge by using domain labels to isolate domain shifts from noise. Performance gains up to 12.5% over seven diverse datasets with three noise types demonstrates DL4ND’s ability to generalize to a wide variety of settings.

Published as a conference paper at ICLR 2026
NOISE-AWARE GENERALIZATION: ROBUSTNESS TO IN-DOMAIN NOISE AND OUT- OFT-OF-DOMAIN GENERAL- IZATION
Siqi Wang, Aoming Liu, Bryan A. Plummer
Department of Computer Science
Boston University (siq) wang, aml iu, bm @bu edu
ABSTRACT
Methods addressing Learning with Noisy Labels (LNL) and multi-source Domain Generalization (DG) use training techniques to improve downstream task perfor- mance in the presence of label noise or domain shifts, respectively. Prior work often explores these tasks in isolation, and the limited work that does investigate their intersection, which we refer to Noise-Aware Generalization (NAG), only benchmarks existing methods without also proposing an approach to reduce its effect. We find that this likely due, in part, the new challenges that arise when exploring NAG, which does not appear in LNL. or DG alone. For example, we show that the effectiveness of DG methods is compromised in the presence of la- bel noise, making them largely ineffective. Similarly, LNL methods often overfit to easy-to-learn domains as they confuse domain shifts for label noise. Instead, we propose Domain Labels for Noise Detection (DL4ND), the first direct method developed for NAG which uses our observation that noisy samples that may ap- pear indistinguishable within single domain often show greater variation when compared across domains. We find AND outperforms DG and LNL methods, including their combinations, even when simplifying the NAG challenge by using domain labels to isolate domain shifts from noise. Performance gains up to 12.5% over seven diverse datasets with three noise types demonstrates DLAND's ability to generalize to wide variety of setting!
INTRODUCTION
Domain Generalization (DG) methods train models generalize unseen target domains by learn- ing from multiple source domains (e.g., Cha et al. (2022 (02T); Wang et al. 2023); Arjovsky et al. (2019 Kamath al. (2021 : Chen al. (2023) 2024a): : Rame et al. (2022); Lin et al. (2022). How- ever, these methods tend to ignore label noise, which appears naturally in many datasets (e.g.. Chen jet al. (2024b); Xiao et al. (2015) ; Li et al. (2017c), Song et all (011)), including those used DG benchmarks (Teterwak et al. (025a). Despite this, limited prior work has explored the effect of label noise in DG settings (Qiao & Low| 2024 Seo et al. 2020). which simply evaluates existing methods without proposing solutions improve robustness. A naive approach mitigate label noise issues would be simply to combine DG methods with those from the Learning with Noisy Labels (LNL) lit-
erature (e.g.. Nataraja et al. (2033); Arpit et al. (2017): Song et al. (2022): Xia et al. (2022 2023):
Wei et al. (2022): Liu et al. (2021): Song et al. (2024): Cordeiro et al. (223)); (Shen & Sanghavi
(2019); Wang Plummer (2024)). Generally speaking, LNL methods work by detecting potential
noise and then either removing (Mirzasoleiman et al. 2020; Kim et al. 2021). relabeling (Karim)
al. 2022 Li al. 2023), or downweighting (Liu al. [020 (203) these samples. However, as
shown in Figure [] separating distribution shifts due to noise from distribution shifts due to domain challenging since they look similar according to feature similarity or analyzing loss values (an
observation echoed in related work on
detection (Humblot-Renanl et al. 2024
'Code: http:pps//ggghuu..c.c/.ooo// /Noi se- Awa General ization

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How to Separate Label Noise from Distribution Shifts?
Prediction: 0.99 Dog
NAG Challenge: Unable to distinguish solely based on predictions, losses or feature similarities.
Label: Dog
Loss: 0.01
Distribution Shift should be Included from training for generalization.
Prediction: 0.51 Dog
Similarity Score: 0.35
Label: Dog
Loss: 0.67
Prediction: 0.52 Dog
Noisy Label should be excluded from training to avoid overfitting.
Label: Dog
Similarity Score: 0.37
Loss: 0.65
Figure I: A key challenge in NAG is being able to separate distribution shifts due to domain from shifts due noise. However, in practice these samples can be hard distinguish between each other, which makes mitigating the effect of label noise challenging as many LNL methods require some mechanism of detecting the noise (e.g.. [i et al. (2023); Karim This represents new challenge that only emerges when considering learning with noisy labels and domain generalization together, highlighting the need explore NAG.
(2022): Zhao al.) (2024), )
DG methods often ignore in- domain performance (e.g. Sagawa et al.
Figure 2:
Learning with Noisy Labels (LNL) ELR, UNICON, PLM
Noise
Robust DG
2020 Rame et al. (2022); Krueger
ag. GmupDRO, Fish, VREx
et al.
2021); Zhang et al. (2024);
Wortsman al. (2022)). label noise
(e.g. Teterwak et al. (2025b): Cha et al.
[2022
2021]; Wang al. (2023)), or
Noise-Aware Generalization (ours)
both.
LNL methods handle in-domain
In-Domain
Out-of-Domain (Domain Generalization)
label noise, but not domain shifts (Liu
ERMs SWAD, SAGM
ct al.
2020 Li al. [023 Karim et al.
2022
contrast, NAG imitates real-world applications
Zhao et al.] 2024).
Batancing ID+OOD egg. DISAM
by requiring methods that do well on all
data when trained noisy data.
We refer to the intersection of DG and LNL Noise-Aware Generalization (NAG), where the goal to maximize both in-domain (ID) and oft-of-domain (OOD) performance when training on noisy, multi-domain datasets. This makes NAG methods range of settings than DG or LNL. alone. In particular, there are three major components in Figure use compare NAG to related tasks: 1) methods that are evaluated on ID performance, 2) methods that are evaluated on OOD performance, 3) methods that are evaluated when trained with label noise. The benchmarks used by method prior work would cover of these settings most. For example, DG methods either ignore in-domain performance (e.g.. Sagawa et al. 2020); Rame (2022): Krueger et (202T) or label noise (e.g.. Teterwak et al. (2025b); Cha al. 2022
practical as they be applied wider
al.
2021
Wang et al. (2023). ). LNI. methods handle noise, but do not consider domain shifts (Liu
al.
2020 et al. 2023 Karim et al. 2022 Zhao al. (0224). However,
common to deploy
model that should perform well on both ID and OOD data, and label noise is expected it is often
prohibitively expensive to collect perfectly clean data (c.g., where expertise is required or annotators
simply disagree (Chen al. 2024b Tanno et al. 2019 Vorontsov & Kadoury 2021))
We begin by exploring NAG by analyzing the challenges through experiments on synthetic noise dataset, providing foundational insight for further research. In particular, highlight the issues with developing LNL-style methods that aim to detect noise, partly highlighted in Figure [ where identifying that distribution shifts in the training set due to noise rather than domain shifts is
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Visual Similarities can confuse same-domain comparisons
Cross-Domalle comparisons can be more reliable
X
Domain:
Quickdraw
Label: Lion
Label: Lion
Domain: Sketch
Figure 3: Images in example embedding space. Comparing images within the same domain (shown in purple) can rely on spurious features, such as similar colors, resulting in labels being incorrectly identified as clean. However, cross-domain comparisons (shown in red and green) must rely on intrinsic features, thereby providing a more accurate estimate of an image's label.
challenging. A naive solution to this problem would be to separate samples into their respective domains. effect, this would theoretically reduce the problem the traditional LNL task, where method would identify noisy samples within a single domain. However, spurious features in some domains make them more challenging to detect.
For example, the two images labeled as"'ion" in Figure 3 have similar colors, which model may inadvertently use as most lions also have the same colors. Thus, separating them within the photo domain would be harder than across domains where they must rely on intrinsic, domain-agnostic features. This observation motivates our proposed method, Domain Labels for Noise Detection (DL4ND), which identifies noisy samples by extracting (class, domain) proxies from low-loss sam- ples. Then we use these proxies for reliable noise detection through cross-domain comparisons. We show that DL4ND combined with DG methods for further gains. Experiments with 12 state-of-the-art DG and LNL methods, along with 20 combination methods on three real and four synthesized noise datasets, show that DL4ND performs to 12.5% better than prior work.
improve performance over other LNL methods its own, can be
Our contributions are summarized below.
We highlight the challenges of real-world datasets that exhibit both label noise and domain shifts in
diverse fields, including web data (Fang et al., (013) and biological imaging (Chen et al.) 2024b).
We investigate the underexplored setting we call Noise-Aware Generalization (NAG), which fo- cuses on training a robust network under ID noise while ensuring good generalization to OOD data. We analyze the limitations of existing approaches and their naive combinations provide insight into NAG and its challenges.
We propose DLAND, novel noise detection method that can be used its in combination with prior work DG, showing a promising solution to NAG.
RELATED WORK
As discussed in the Introduction and summarized in Figure 2 NAG combines elements from both the Domain Generalization (DG) and Learning with Noisy Labels (LNL), producing new challenges not present when exploring each task alone. DG methods (e.g.. Guirajani & Lopez-Paz (2021b); Li al. 2 2019 2018a b): MMandeet et al. (2013): Teterwak et al. (2025b)) often focus on learning Nomain-invarian
features (i.e., aligning their representations). In contrast, LNL methods typically aim to minimize the effect label noise by reweighting samples based on an estimate of cleanliness (c.g., Scott (2015); Liu & Tao (2015); Menon et al. (2015); Patrini et al. (2017); Li et al.
et al. (2021): :2 Zhang et al. (2021); 0: Kye et al. (2022) y Cheng et al. (2022); Liu et al. (2023
(202bb) Vapnik et al. (2015) Yong a (2022)) or detecting and removing (or relabeling) the noisy
samples (e.g., Hu et al. (202 1); Torkzadehmahani et al. (2023): Nguyen et al. (200)): Tanaka et al.
2018): Hou al. 20255
al. (0022a); Feng al. (2022) Li et al. 2023
Song et al. (024))
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Xia et al. (2023); Cordeiro al. (2023): Wei et al. (2022): ) Xia et al. (2022)). However, DG methods often ignore the effect of label noise and only evaluate on out-of-domain (OOD) generalization. In contrast, LNL methods study the effect of label noise in-domain (ID) data, but ignore domain shifts. Thus, these methods may not generalize in real-world settings like explored by NAG, where (at least some) label noise is expected and models have perform well II) and OOD settings.
While there is limited DG work that evaluates based on ID and OOD performance (e.g.. Zhang et al. (0224) WWotsman et al. (2022))
), these methods do not explore the effect of label noise. Similarly, prior work that evaluates existing DG method's performance with label noise do not develop new techniques for this setting (Qiao & Low, 2024 Seo al.) 2020). only exploring preexisting ap- proaches. Some work that explores the intersection of Domain Adaptation (DA) and LNL proposes custom methods (e.g.. Shuu al. (2019); Han et al. (2023); Zuo al. (2022; ; /huo al. (2023); Yin et al. (2025)), but as DA is typically performed single source domain, they
Feng et al. (2023); y do not have to learn separate domain shifts from label noise (i.e., they do not have to address the challenge in Figure D As such, many of the DA+LNL methods closely match those in the LNL literature (see Appendix B for examples), with similar limitations. Additionally, prior work in DG+LNL and DA+LNL are typically not evaluated iments show, these limitations mean that they do not generalize well to NAG, often performing on par with simple ERM (Gulrajani & Lopez-Paz 2021a). Thus, exploring NAG is a necessary step to creating methods that generalize to real-world settings.
their in-domain performance. As exper-
NOISE-AWARE GENERALIZATION (NAG)
Consider a multi-domain dataset 'D with m source domains: D = (D,, D2,.... Dm where each D; { " represents samples from domain with æa. as the input and Di,j as the label, potentially noisy and the true label yi. is unknown. The goal in NAG is to learn a featurizer fe(.) parameterized by 0 that performs well in all source domains {Dr
Jm and generalizes to unseen domain(s) D Ptarget. despite the presence of label noise. For convenience in describing the equation in the rest of the section, denote the domain of input (xr) and its class label Y(r). We use d(-) to represent the cosine distance between feature embeddings.
To help better understand NAG, we perform preliminary analysis using RotatedMNIST GGhifary ett al. 2015). chosen for its simplicity and clear feature structure. Note that we also validate these observations on more complex datasets in experiments. We select four rotation angles (0", 15%, 30,, 45%) to represent different domains. Pairwise noise is introduced by randomly flipping 30% of the labels between four confusing digit pairs: (0, 6), (1, 7), (3, 5), and (4, 9). Our experiments use ResNet50 (He et al., 2016) trained via ERM (Gulrajani & Lopez- Paz 2021a).
3.1 CAN WE SEPARATE CLASS SHIFTS FROM DOMAIN SHIFTS?
Detecting samples that may stem from label noise a key capability for LNL-style methods. How- ever, as NAG also requires generalizing across domains, an important capability that distinguishes NAG from related work is being able separate samples with shifts due to domain from shifts due to label noise during training (partially highlighted in Figure D.. However, measuring distributional differences between samples (e.g., by computing feature distances between all sample pairs) can be computationally expensive. Thus, LNL methods often rely having some canonical representa- tion of a class to measure similarity of a particular sample (e.g., MMrzasoleeman et al. (2020); Song al. 2024); Zhao al. (2024)), ), for example, by averaging the features of samples of that class. If shifts in domain were closer to this canonical representation than shifts in category, then we could successfully identify potential noise.
More formally, continuing the earlier notation, let us group samples in domain-class sets, i.e.,
Ge {æz (z,) c, D(x,) = i}.
Let go.k = [o..el fe(G...) denote the average of the set of learned features for group Ge We assume the existence of featurizer fo(-) such that:
d(fo(G De.)) d(Jo Tei), where + and é.
(2)
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(a) Distances From All Samples
(b) Distances From Low-Loss Samples
0.030
0.14
0.025
Distance
0.08
0.015
0.06
0.010
0.04
0.02
0.000
0.00
4
and
Different
Different 9
4 and 9
Different
Different 9
Figure 4: Box plot of distance distributions between samples and their canonical class repre- sentation Ic.s- (a) Reports distances between training samples and an fo computed by averaging all training samples in its class, domain group Ge. The red box highlights overlapping distributions, indicating the challenge of distinguishing samples with class and domain shift. (b) Je.i is calculated from low-loss samples which shows shifts in class can be separated from shifts in domain. See Sec- tion 3.1 for additional discussion.
This suggests that we can perfectly separate class shifts from domain shifts if we can train featur- izer such that, for each sample, the distance to other samples within the same class (across different domains) is smaller than the distance to samples within the same domain (but different classes).
Figure explores this possibility by showing the distribution of distances in the presence of both shifts domain and shifts in class on RotATedMNIST (Ghifary al.] (015). As seen in the red ( 4a). where Do,i is computed over all samples in the group G.... there is significant
box in Figure portion of the shifts in category and domain that cannot be distinguished from each other (verifying in practice). Thus, Fquation (2) is not satisfied and we cannot use this approach to identify noisy samples. However, as shown in Figure 4b), we find that we can satisfy Equation (2) by creating Ic, with samples have high confidence
the example in Figure
not noise. For example, by using only low-loss samples early in training (i.e., before the model begins overfitting) prior work has shown typically clean (Liu al.) 2020 Choi al. 2025). See further validation of these observations on more complex datasets in Section
3.2 IS IT REALLY THAT IMPORTANT TO SATISFY EQUATION
As discussed in the previous section, if we are not careful about how we measure feature similarity between samples then we can end up in situation like for samples in red box in Figure Aa), where we simply can't distinguish between shifts due to class and shifts due to domain. However, one could argue that if we are unsure about these samples, we should simply mitigate their contribution to model training (e.g., by downweighting their loss or removing them entirely). Lee., if we can still learn the decision boundary between classes without them, then these samples unnecessary.
To quantify sample importance to the final decision boundary, we trained an SVM on RotatedM- NIST and treat the resulting support vectors the "important" samples. We find that over 20% of the support vectors fall within the red box region Figure 4[). Thus, as we show in Section removing their contribution during training has significant effect on the final decision boundary.
4 DOMAIN LABELS FOR NOISE DETECTION (DL4ND)
In Section gave formal definition of NAG and also provided analysis that lead to means of separating domain shifts from category shifts by using a small set of samples to create a proxy represent the canonical features of particular category. If all datasets were simple as Rotat-
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(closs, domain) Prosies the Embeddling Space
Samples
Lobet Dog
Domaix:
!
Labet Dog
Domain:
M
A dog proay
oot procy
Labet
High Samples
Labet Dog
A
Domalt:
Label
Labet Dog
Domale:
Training Data
Loss Destibution
Sample Separation
Cross Domain Comparisons
Updareed Labels
Figure 5: DLAND Framework. After brief warmup stage, the first step in DLAND is split samples into low-loss and high-loss groups using a Gaussian Mixture Model (GMM) based on the loss distribution. The low-loss sample's labels are frozen and used to generate (class, domain) proxies Ge.i. High-loss samples are relabeled using the proxies with Equation B Then training resumes with the updated labels. Note that all stages, DG method (e.g., ERM++ (Teterwak al.
2025b), SAGM (Wang al. 2023), SWAD (Cha et al., 2021) can also be used in combination with DLAND. See Section
for additional discussion.
cdMNIST, this might be sufficient solve the NAG task. However, more complex datasets there can be many confounding factors, such as due to biases in a source domain (see Figure 3 that can confuse a model. Section 4.1 aims to reduce the impact of these spurious features via DL4ND, our proposed approach for detecting noise by using cross- how we fit our DL4ND noise detection method into our full NAG framework. See Figure for an illustration summarizing our approach.
comparisons. Section 4.2 describes
4.1 DETECTING NOISE WITH CROSS-DOMAIN COMPARISONS
As illustrated in Figure 3 noisy samples may exhibit strong visual similarity to their incorrect noisy labels within given domain. This "visual similarity" often arises from spurious features, such as background or color, which are domain-dependent and may not persist across different domains. As will discuss further below, these issues persist even if were simplify the NAG problem by using domain labels to keep training samples in their own domain, i.e., to perform single-domain comparisons to identify noise. These observations lead us to formulate a hypothesis: can cross- domain comparisons provide a stronger signal for a sample's true class than single-domain methods?
Let us perform a thought experiment with the example in Figure 3] Since the lion images in the photo domain have a quintessential set of colors used for typical lion, its reasonable that model might use these correlations in color space to identify an image is a lion. After all, an image contained largely black pixels, then the model knows it is unlikely to be a lion in this domain. However, other domains may not have the same color bias, e.g., the sketch and draw images in Figure Thus, comparing images from these domains to the photos requires the model to rely more lion's intrinsic features that generalize across domains. Motivated by this observation (which empirically validate in Section 5), DLAND creates potential noise by finding the closest class representation from another domain Je i.e,;
label S for sample x; identified as
J. = arg min fo (zx,), (e.)), where i * 7925
(3)
Note that this does not mean that Di * yi in all cases, as some x; may have their labels judged as correct according to Equation 3b. Next we discuss how Equation (3) is used in our full framework.
4.2 DLAND FRAMEWORK
Prior work showed noisy labels have minimal effect on early stages of training (Liu et al. 2020). In this stage the concept of category is still being formed, often using general, easy-to-leam features.

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Thus, LNL methods like DLAND typically applied in later stages where model may start overfitting to noisy labels. This means we begin training using ERM (Gulrajani & Lopez- Paz. 2021a), any other DG method, which is used in all stages of training. After this warmup step, first create our class proxies using the low-loss samples inspired by the discussion from Section 331
Specifically, rather than introducing hyperparameter that manually controls the loss threshold, assume the loss distribution is separated by a Gaussian Mixture Model (GMM) with two clusters. Samples belonging the low-loss cluster serve as proxies, while high-loss samples require label updates through cross-domain comparisons. Low-loss samples are grouped by both domain and class (as shown Equation (T). This means each (domain, class) pair has its own proxy represen- tation, computed as the average feature of all low-loss samples in the same (domain, class) group. We assume these low-loss samples have clean labels and are kept frozen during training.
High-loss samples are relabeled using the cross- domain comparisons defined in Equation (3). Thus, DL4ND performs label refinement during training, requiring no additional data or learning overhead. This relabeling can be done periodi- cally, as done in prior work Karim et al. (2022); experiments show that labeling only once can improve label qual- ity. For example, Table [ shows DLAND Ro- latedMNIST increased label accuracy from 75% to 98%, resulting in a 10% boost in both ID and OOD accuracy.
Table 1: Top-1 accuracy on RotatedM- NIST (Ghifary et al. (0155) with 30% asym- metric label noise. The baseline method creates fo using all samples from its group Ge.² with single-domain rather than cross-domain com- parisons. See Section 4.2 for discussion.
Li et al. 2023). However,
Method
Label Acc.
ID
OOD
Baseline
75.7
87.7
87.9
DLAND (ours)
98.1
98.1
97.8
EXPERIMENTS
Datasets. We use three real-world datasets (VLCS (Fang al. 2013). CHAMMI-CP (Chen ct al. 2024b), and PACS (Li et al., 2017a)) and three additional synthetic noise datasets (Office- Home (Venkateswara et al. 2017). Terralncognita (Beery et al. 2018). and DomainNet (Peng al. 2019) to supplement our RotatedMNIST experiments reported earlier. Appendix C discusses our noise types, which supplements the results using asymmetric noise on RotatedMNIST with real- world highly noisy dominant noise (Wang& & Plummer 2024) (e.g., CHAMMI-CP) with both real and synthetic asymmetric and symmetric noise (VLCS and PACs for real noise, and DomainNet, Officehome, and TerraIncognita for synthetic). These datasets provide results over ride range of applications, e.g., VLCS and DomainNet are typical web images, CHAMMI-CP contains cell images, and Terralncognita contains images from wildlife cameras.
Metrics. All of our datasets report top-1 classification accuracy on both ID and OOD data. In some experiments we also report the average of these two metrics to measure overall NAG performance.
Experimental setup. As shown in Figure 2] there are three components to evaluating NAG: in- domain (ID) performance, out-of-domain (OOD) generalization, and robustness to noise. Our periments are designed to evaluate each component by reporting both ID and OOD accuracy using a "leave-one-out" protocol used by domain generalization benchmarks like DomainBed (Gulrajani & Lapez-Paz 202 In other words, we train on all domains but one, compute accuracy on the test sets for both ID and OOD data, and average the results. Robutness to label noise is measured using real-world noise (i.e., noise that appears naturally) as well as controlled experiments like those we conducted using RotatedMNISTT in Section 3 and Section 4 Real-world noise experiments vali- date that our approach works in practice, while the controlled experiments enable us to provide into deeper insights by controlling the noise that appears the datasets.
Following Ballas & Diou (005): Gulrajani & Lopez-Paz (2011)): Wang et al. (2025), ), our exper-
iments use a ResNet-50 (He et al., 2016) pretrained on ImageNet (Deng et al. CHAMMI-CP which, following Chen al. (2024b), uses ConvNeXt (Liu et al.
2009). except for
2022 pretrained on ImageNet 22K. While some prior work has used large multimodal models (LMMs) in their experiments, LMMs have applications restrictions due to task computational requirements. E.g., CHAMMI-CP (Chen et al. (024b) has up to 5 channel cell images, so LMMs that process 3 channel natural images do not apply. Additionally, recent work has shown the high performance of LMMs

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Best DLAND
ENL
DG
SAGM-SWAD
Combo (ours)
DG
Best DL4ND Combo (ours)
Best LNL+DG Combo
DLAND (ours)
DLAND (ours)
SAOM ERM
Best LNL+DG
Combo
ERM
PLM
ERM+
ERM
PLM
ELR
SAGM
SAGM+
SWAD
ID
ID
(a) VLCS (Fang et al.
2013
(b) CHAMMI-CP (Chnn al. 2024b)
Figure 6: Summary of real-world noise top-1 accuracy changes on VLCS Fang et al. 2013) and
CHAMMI-CP (Chen al. and report relative absolute changes in both in-domain (ID) and out- domann (OOD) performance for the best LNL (ELR (Liu et al., 2020), PLM (Zhao et al. 2024 ) and DG methods (ERM++ (Teter- wak et al. 2025b), SAGM (Wang et al. 2023), SWAD (Cha et al. (02T)). Appendix A contains additional methods and combinations. We find DLAND outperforms prior LNL methods, even when combined with DG approaches. See Section 5.1 for further discussion.
2024b). We center results around ERM (Gulajjani Lopez- Paz 2021a),
Table 3: Results with 60% symmetric noise on Office- Home (Venkateswara et al. 2017) (see Table 4 for asym- metric noise). Using DL4ND provides up to 12.5% gain.
Table 2: Real-world noise top-1 accu- racy on PACS (Li et al. 2017a). In- tegrating DLAND with DG methods provides up to a 2% average gain.
Method
ID
OOD AVG
Method
ID OOD AVG
ERM
45.8
40.5
43.2
w/ DLAND (ours)
47.9
49.9
48.9
SAGM
96.3
85.3
90.8
SAGM
48.6
40.3
44.4
w/ DLAND (ours)
97.3
88.8
93.1
w/ DL4ND (ours)
52.0
52.6
52.2
ERM++
96.7
89.2
92.9
ERM++
56.7
48.7
52.7
DLAND (ours)
96.5
90.1
93.3
w/ DL4ND (ours)
60.3
59.4
59.8
on DG benchmarks like VLCS and OfficeHome is largely due to train/test contamination [Teterwak] ct al. 2025a), which is significantly less pronounced in Terralncognita. This also likely explains why some of our baseline methods outperform LLMs like GPT-4v (by up to I 15%) reported in Han et al. (024 Terralncognita using a ResNet-50. Additional implementation details are in Appendix
5.1 RESULTS
summarizes key results comparing our DLAND approach with top methods from the LNL and DG literature, along with their best combinations, on VLCS (Fang al.] 2013) and CHAMMI- CP CCen et all 2024b) (additional results in Appendix A). We make two notable observations. First, our DLAND method on its own outperforms other LNL methods, and DLAND is the only approach on CHAMMI-CP that provides an average boost on its own. Second, DLAND combined with DG methods outperforms combinations of prior work in DG+LNL by 1-2% on average. Table strates that the advantage from DLAND also extends to PACS (Li et al. 2017a), booting performance when combined with DG methods by up to 2%. These results show that DLAND beneficial across diverse datasets with real-world noise, especially when combined with DG methods.
Figure
Table 3 and Table 4 provides results on a controlled experiments where we add symmetric noise to OfficHHome (Venkateswara et al. 2017) and asymmetric noise to OfficeHome, TerraIncog- nita (Beery et al., 2018), and Domain et (Peng et al.] 2019), respectively. Overall, DLAND results in boost performance in most settings. obtaining the best performance in 11 of 13 cases, with gains as large as 12.5% (OOD results on symmetric OfficeHome). We find that as we increase the amount of noise in a dataset that the benefit of DI DLAND also grows.
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Table Asymmetric noise results on OfficeHome Vennattswara al., 2017). TerraIncog- nita (Beery et al. 2018), and DomainNet (Peng et al., 2019). We bold overall best results, and underline best results between DLAND and specific DG method it is integrated with. DLAND boosts performance in of 10 settings, with a 9% gain DomainNet. More results are in Table
OfficeHome
TerraIncognita
DomainNet
Method
No Noise 20% Noise 40% Noise No Noise 20% Noise 40%
Noise
40% Noise
ID OOD ID OOD ID OOD
ID OOD ID
OOD
ID
OOD
ID
OOD
SAGM
83.4 69.1 76.7
64.0
62.0
52.0
86.7 51.3 78.2
40.4
56.4
30.9
43.6
26.9
w/ DLAND (ours)
81.4 66.6
69.3
55.0
77.1
43.5
58.7
32.8
52.8
35.6
ERM++
85.1
71.7 78.5
65.8
63.1
52.3
85.9
47.5
75.9
45.0
56.1
30.6
42.4
28.9
w/ DLAND (ours)
76.6
68.6
62.7
56.4
73.0
43.2
57.0
37.5
51.1
36.2
Table 5: Ablation study on VLCS (Fang al. 2013) and CHAMMI-CP (Chen et al. 2024b). We bold overall best results, and underline best results between when DL4ND is integrated and its ablations. We show our model components boost performance by 2-4%.
Method
VLCS
CHAMMI-CP
ID
OOD
AVG
ID
OOD
AVG
SAGM+SWAD w/DLAND (ours)
91.9
88.6
90.6
76.6
47.3
61.9
w/o relabel
88.5
87.2
87.8
70.9
47.2
59.0
w/o cross-domain
88.6
87.8
88.2
68.5
49.0
58.8
w/o small-loss proxy
88.2
87.0
87.6
73.6
45.8
59.7
ERM++ w/DLAND (ours)
95.3
89.0
92.2
72.9
44.3
58.6
w/o relabel
89.5
88.8
89.2
72.5
45.4
58.9
w/o cross-domain
89.5
87.6
88.6
69.7
47.3
58.5
w/o small-loss proxy
93.5
87.7
90.6
68.9
43.4
56.1
Table provides an ablation study that shows each component of DLAND improves performance. Specifically, w/o relabel refers to removing rather than relabeling samples, w/o cross-domain uses within-domain rather than cross-domain comparisons and w/o small-loss proxy reports results that uses all samples to create the proxies for cross-domain comparisons rather than just low-loss samples (discussed in Section 3.I.. As shown Table each component provides 2-4% gain in most settings, with the best performing method on each dataset using all components. In Table least be partly explained as improvement in the precision of relabeled samples.
identify noise (described Section 4.1.
6 we also show that the gain from cross-domain comparisons can at
5.2 WHY DON'T NAIVE LNL+DG COMBINATIONS PERFORM BETTER?
At high level, DLAND resembles UNICON (Karmm et al. 2022), which also estimates and relabels potential noisy samples. However, as discussed in the Introduction, LNL methods like UNICON assume that high-loss samples tend be noise, but in NAG these samples may also be domains that are harder to learn. For example, Figure shows that samples selected as part of the clean subset when training on VLCS mostly come from LabelMe, whereas some domains like SUN09 are mostly identified as noise. This makes it challenging to learn from all domains as some may have many samples incorrectly relabeled (especially those along the decision boundary). In effect, this relabeling introduces more noise rather than fixing it. Figure in the Appendix shows these issues persist even when decreasing ratio samples allowed to be relabeled. This observation similar to the one that UNICON made of DivideMix (Li al. 2020). where they noted that some categories were more prone to relabeling than others and argued for more balanced approach to address it.
In Table 7 we extend this idea to the multi-domain setting, where ensure relabeling is also source- balanced. Specifically, we perform per-domain sampling, where we relabel only a subset of each domain (Le., relabel 20% of the samples in each domain rather than 20% of the dataset regard-

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Table 6: Comparing relabeling precision at two noise levels on OfficeHome (Venkateswara et al., 2017) and Terralncognita (Beery al. 2018). Our cross-domain comparisons improves preci- sion by up to 10% over using a single domain.
Comparison
OfficeHome
Terralncognita
20%
40%
20%
40%
same-domain
99.5
94.0
82.5
64.3
cross-domain 99.8
97.2
86.7
74.3
Figure 7: Changes in domain distribu- tion after the UNICON sample selection process on VLCS (Fang et al. (013). (Left bar: namber of samples before selection, right bar: after selection). These two cases illustrate a risk of skewing domain distribu- tions from the LNL selection process. See Sec. 5.2 for more details.
Class Car
Class: Person
Orginal Sclooed
city na Selected
Number of Samples
SUNG9
VOC2007
LabetMe Caltedh101
SUN09
Table 7: Comparison of relabeling methods on real-world noise top-1 accuracy on VLCS (Fang 2013) and CHAMMI-CP (Chen et al., 2024b). We use the best performing DG method SAGM (Wang et al. SSWAD (Cha et al. 2021) and combine them with to relabeling method UNICON (Krim et al.J 2022) or DLAND. Per-domain sampling uses domain labels to separate samples by their sources, and relabels samples evenly across domains. This effectively reduces to standard LNL task addressed by UNICON, since each source does not contain domain shifts. However, DLAND still performs better, especially on VLCS. See Section 5.2 for discussion.
DG Method
LNL Method
Per-domain
VLCS
CHAMMI-CP
sampling
ID
OOD
AVG
ID
OOD
AVG
UNICON
89.9
84.0
86.9
76.7
42.0
59.4
SAGM+SWAD SAGM+SWAD SAGM+SWAD SAGM+SWAD DLAND (ours)
91.4
87.7
89.5
78.3
41.5
59.9
UNICON UNICON
87.8
82.8
85.3
75.4
43.3
59.3
89.3
86.8
88.1
77.0
46.3
61.6
91.9
88.6
90.3
76.6
47.3
61.9
less of domain). Table shows this does improve the performance of combining UNICON with DG methods SAGM+SWAD by 2-3% average. However, it only improves performance com- pared to SAGM+SWAD alone on CHAMMI-CP, and actually decreases performance on VLCS. This is likely due, in part, the fact that in this setting UNICON will still utilize same-domain comparisons, which we showed in the previous section underperforms DLAND's cross-domain ap- proach. Thus, seen comparing the last two lines of Table 7 our approach is 2% better VLCS while also providing a small gain on CHAMMI-CL Notably, DL4ND improves performance over SAGM+SWAD alone on both datasets unlike UNICON.
CONCLUSION
This work addresses the challenges of training noisy, diverse real-world data by exploring Noise- Aware Generalization (NAG), which focuses on handling in-domain noise and improving out-of- domain generalization. We highlight several key takeaways. First, NAG presents new challenges, which complicate the task of distinguishing between noise and domain distribution shifts. Second, naive combination of LNL and DG does not effectively address this task. Domain shift can interfere with noise detection, and LNL-based sample selection can inadvertently skew the domain distri- bution. Lastly. we demonstrate that using cross-domain comparisons as a critical signal for noise detection significantly improves performance. Noise, which lacks the intrinsic class features, fails to exhibit closer distances to other domains. Experimental results validate the effectiveness of our approach, and the discussion also provides insights for further advancing NAG.
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Acknowledgments. This study was supported by the National Science Foundation under NSF-DBI award 2134696. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the supporting agency.
ETHICS STATEMENT
This paper addresses NAG, a task that requires models to perform well on both in-domain and out-of-domain data when training on datasets with label noise. This result in models that can effectively learn from a wide variety of data, including cell painting data where prior work in tasks like LNL found especially challenging due to its high amounts of label noise (Wang & Plummer, 2024). However, like many topics in this field, also can enable bad actors to use these models to train more effective recognition systems for nefarious purposes. Additionally, users should be mindful that although we provide an evaluation on a diverse set of datasets, they still make mistakes in their predictions that may vary depending on the dataset. Thus, researchers and engineers should be mindful of these factors when deploying a system for end-users.
REPRODUCIBILIIY STATEMENT
We have released our code and data ensure it be reproduced. We provide detailed implemen- tation details as well as an in-depth example of how to integrate the methods in our experiments in Appendix [] Our released code is capable of training and testing the models we compared to in unified codebase. This enables additional methods to be easily integrated and the data loaders required evaluate models on our benchmarks. Further, we have also included some pretrained models for ease of use.
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Qi Wei, Haoliang Sun, Xiankai Lu, and Yilong Yin. Self-filtering: noise-aware sample selection for label noise with confidence penalization. In European Conference on Computer Vision, pp- 516-532. Springer, 2022.
Mitchell Wortsman, Gabriel IIharco, Jong Wook Kim, Mike Li, Simon Kornblith, Rebecca Roelofs, Raphael Gontijo Lopes, Hannanch Hajishirzi, Ali Farhadi, Hongseok Namkoong, et al. Robust fine-tuning of zero-shot models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 7959-7999999 7901999911211 2022.
Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Jun Yu, Gang Niu, and Masashi Sugiyama. Sample selection with uncertainty of losses for learning with noisy labels. In International Confer- ence on Learning Representations, 2022. URL htttp::/ net/ forum?ouummmmmmm fmmidd= XENE
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meet/forumiid
T4WB.3HA3dT
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Table 8: Comparing methods of addressing NAG on VLCS Fang al., 2013) and CHAMMI- CP (Chen et al. 2024b) either via (a) by using a method from prior work in the DG literature or (b) LNL methods like our approach DL4ND approach. We bold the best performance overall and underline the best LNL method. We find DLAND performs best among LNL methods, and does especially well on CHAMMI-CP. See Appendix discussion.
Method
VLCS
CHAMMI-CP
OOD
AVG
ID
OOD
AVG
(a)
ERM Gulrajani Lopez-Paz
2021a)
88.5
84.6
86.6
77.1
42.5
59.8
VREx
Krueger al. 2021
89.0
84.4
86.7
74.8
44.8
59.8
SWAD CCha et al.
2021
90.8
86.2
88.5
73.9
43.7
58.8
Fishr Rame
2022
88.9
85.8
87.4
73.9
44.0
59.0
MIRO Cha et al. 2022)
88.9
83.8
86.4
65.5
46.6
56.0
SAGM Wang et al.
2023
91.0
87.2
89.1
77.1
41.2
59.2
DISAM Zhang et al. [2024
89.6
84.9
87.2
72.4
44.8
58.6
ERM++ Teterwak et al.
2025b)
90.9
86.6
88.7
72.5
44.6
58.5
MIRO+S WAD
88.9
83.7
86.3
67.3
45,8
56.6
SAGM+SWAD
91.4
87.7
89.5
78.3
41.5
59.9
(b) ELR (Liu et al. 2020
90.3
82.3
86.3
76.8
43.6
60.2
UNICON (Karm et al. DISC (Li et al. 2023)
2022
89.9
84.0
86.9
76.7
42.0
59.4
88.7
82.5
85.6
43.3
41.3
42.3
PLM (Zhao al. 2024
87.9
82.6
85.2
70.5
44.4
57.5
DL4ND (ours)
90.5
86.8
88.6
74.6
46.0
60.3
Yivan Zhang, Gang Niu, and Masashi Sugiyama. Learning noise transition matrix from only noisy labels via total variation regularization. In International Conference Machine Learning, pp- 12501-12512. PMLR, 2021.
Rui Zhao, Bin Shi, Jianfei Ruan, Tianze Pan, and Bo Dong. Estimating noisy class posterior with part-level labels for noisy label learning. In Proceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition, pp. 18809-22819, 2024.
Junbao Zhuo Shuhui Wang, and Qingming Huang. Uncertainty modeling for robust domain adap- tation under noisy environments. Trans. Multi., pp- 6157-611777 January 2023.
Yukun Zuo, Hantao Yao, Liansheng Zhuang, and Changsheng Xu. Seek common ground while reserving differences: model-agnostic module for noisy domain adaptation. IEEE Transactions Multimedia, 24:1020-1030 2022. doi: 10. N0.1099/TMMM22221309979999
A EXTENDED RESULTS
Table contains detailed results for both DG and LNL-only methods. Generally speaking, the results follow intuition, where DG methods perform better on VLCS, which has less noise and more dramatic domain shifts. In contrast, DL4ND being an LNL method performs the best CHAMMI- CP, which has more noise and smaller shifts. That said, our approach does outperform other LNL methods on VLCS by 2%. Notably, for DG methods the more regularization focused methods like SWAD are necessary to get best performance. This observation is key as in the LNL methods ELR also regularization-style approach and performs similarly to DLAND on CHAMMI-CP. Thus, conceptually approach has more differences than ELR compared to the DG methods that do well on our NAG task.
As shown in Table D. this conceptual difference manifests itself when we aim to combine DG and LNL methods. Notably, while ELR actually performs worse on CHAMMI-CP, suggesting that the model becomes constrained with the additionl of multiple types of regularization, DL4ND performs better, getting 2% boost on CHAMMI and a 3.5% gain on VLCS when combined with DG method (comparing the last line of Table B to the last two lines of Table 9). The effectiveness of
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Table 9: Comparing different combinations of DG methods on VLCS Fang et al. 2013) and CHAMMI-CP (Chen et al. 2024b) with (a) LNL methods from prior work or (b) our DLAND approach. We find combinations with our DL4ND method gets best performance. See Appendix for discussion.
DG Method
LNL Method
VLCS
CHAMMI-CP
ID
OOD
AVG
ID
OOD
AVG
(a)
ERM+
ELR
89.7
85.4
87.6
75.7
42.0
58.9
MIRO+SWAD
ELR
91.5
86.7
89.1
70.7
44.8
57.8
MIRO
ELR
90.8
84.5
87.7
74.5
41.3
57.9
SWAD
ELR
92.0
87.9
90.0
73.5
44.7
59.1
MIRO
UNICON
89.8
83.4
86.6
77.0
43.4
60.2
MIRO+SWAD
UNICON
88.9
83.7
86.3
76.0
45.7
60.8
SAGM+SWAD
UNICON
87.8
82.8
85.3
75.4
43.3
59.3
(b)
VREx
DL4ND (ours)
91.2
87.0
89.1
75.3
46.7
61.0
Fishr
DL4ND (ours)
89.9
86.5
88.2
73.8
46.1
60.0
MIRO
DLAND (ours)
93.5
86.7
90.1
70.4
46.7
58.5
MIRO+SWAD
DL4ND (ours) 91.7
88.0
89.9
71.2
46.6
58.9
SAGM
DL4ND (ours)
91.9
88.4
90.1
76.2
46.6
61.4
SAGM+SWAD
DL4ND (ours)
91.9
88.6
90.3
76.6
47.3
61.9
ERM++
DLAND (ours) 95.4
89.0
92.2
72.9
44.3
58.6
Table 10: Comparison of relabeling methods on real-world noise top-1 accuracy on VLCS Fanng al. 2013) and CHAMMI-CH (Chen al., 2024b) expanding on our results from Section 5.2 Per-domain sampling uses domain labels to separate samples by their sources, and relabels samples evenly across domains. DLAND outperforms UNICON nearly all cases.
DG Method
LNL Method
Per-domain
VLCS
CHAMMI- CP
sampling
ID
OOD
AVG
OOD
AVG
MIRO
UNICON UNICON UNICON DLAND (ours)
91.2
85.8
88.5
76.9
45.2
61.1
MIRO+SWAD SAGM+SWAD SAGM+SWAD
90.6
86.0
88.3
76.5
43.6
60.0
/
89.3
86.8
88.1
77.0
46.3
61.6
91.9
88.6
90.3
76.6
47.3
61.9
the DG methods also changes when combined with DLAND, as SAGM+SWAD performed best in Tab VLCS, but get nearly 3% gain combining ERM++ with DLAND in Table
In addition, as we discussed Section 5.2 part of the reason why UNICON does not perform as well as DL,NND in Table due the fact that it tends to be biased towards detecting samples from a subset of domains as noise. Lee., harder to learn domains incorrectly get identified as largely noise. Table exxxpdd on our results from the main paper where assume we are provided with domain labels, which enables us to per-domain sampling. On VLCS all the UNICON results underperforms DL4ND by 2%. The various combinations of UNICON with DG methods generally perform the same (even though they do all outperform combinations in Table 19. with the only real variation combing on CHAMMI-CP, However, even on this dataset our DLAND reports a slight advantage, mostly due to stronger OOD performance.
Table IT reports an ERM baseline (Gulrajani Lopez-Paz] 2021a) on own and when combined with approach OfficeHome (Venkateswara et al. (207)) and Terralncognita (Beery et al. 2018) to supplement the results the main paper. We find that DLAND improves performance by 3-18%, demonstrating that approach provide significant benefit over DG methods alone.
Table 12 and Table 13 provides a sensitivity analysis on warmup length on PACS (Li et al. 2017a and Terralncognita (Beery al. 2018), respectively. Notably, the warmup length either dataset relatively short, but should be aided by validation set to optimize its length. That said, as the training dynamics during warmup does not change, setting the warmup length can be done in single
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Table 11: Synthetic noise results on OfficeHome (Venkateswara al. 2017) and TerraIncog-
nita (Beery et al. 2018) to supplement results from Section 5.1 3-18% over ERM (Gulrajani & Lopez-Pas Paz 2021a) alone.
DLAND boosts performance by
OfficeHome
Terralncognita
Method
No Noise
20% Noise 40% Noise
No Noise
40% Noise
ID
OOD
ID
OOD
ID
OOD
ID
OOD
ID
OOD
ERM
80.6
65.6
71.9
59.6
57.8
46.6
84.1
46.3
53.0
33.8
w/ DLAND (ours)
80.2
64.7
68.4
54.1
56.3
37.2
Table 12: Sensitivity analysis to warmup length on real-world noise top-1 accuracy on PACS LLi et al. 2017a). See Appendix A for discussion.
Warmup Length
ID
OOD
AVG
w/o DLAND
96.3 85.3 90.8
300 steps
96.7
84.9
90.8
600 steps
97.3
88.8
93.1
1000 steps
97.2
88.6
92.9
run (i.e., using a validation set to identify when performance gains start to decelerate). In addition, performance on PACS especially shows that the exact length choice can be relatively insensitive.
A.1 CHANGING SAMPLE SELECTION RATIOS
Figure shows the relationship between domain balance, clean sample count, and ID/OOD perfor- mance for the "person" class in VLCS. At lower selection ratios (r), the selected samples are cleaner but the distribution skews toward the cleaner VOC2007 domain, while higher ratios maintain bal- ance but increase noise. The best results occur at r = 0.2, indicating that quality outweighs quantity for improved robustness.
B COMPARISON TO DA+LNL METHODS
As discussed in our related work section, where are several methods that explore the intersection
of Domain Adaption (DA) and LNL (e.g.. Shu et al. (2019): Han al. (2023); Zuo al. (2022); Zhuo et al. y Feng et al. 2023); 2 et al. (2025) ). DA their goal is to improve performance on target domain when given a labeled source domain and unlabeled samples from a target do- main. However, in DG, which we study, the goal to generalize to unseen distributions, thereby requiring different methods. Additionally, they are evaluated on how well they perform on the target domain, which is similar to DG methods that evaluate performance only on unseen domains (ig- noring source domain performance). As we show, this often results in performing well only on unseen domains, thereby limiting its applications when both ID and OOD performance is desired (e.g., MIRO in Table ((a) obtains a 4% boost to OOD performance on CHAMMI, but at a cost to 12% ID performance).
Further, when we consider adapting the ideas of these methods to our task, we find that many of them are well represented in the LNL. literature. For example, Yin et al. (2025); Zuoo et al. (2022)
Table 13: Sensitivity analysis to warmup length on 20% asymmetric noise on TerraIncognita (Beery al. 2018). See Appendix for discussion.
Warmup Length
ID
OOD
AVG
w/o DLAND
78.2
40.6
59.3
1000 steps
77.4
41.2
59.3
1200 steps
77.1
43.5
60.3
1500 steps
77.1
41.4
59.3
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Test Caltech101
1000
30:32:38
800
30:30:40
LabalMe
90
Sample Count
Accuracyy (%)
600
28:31:41
83
27:29:44
75
70
0DD
Perforance
0.6 Sample Selection Ratios
Figure 8: Balance, clean sample ratios, and ID/OOD performance on VLCS "Person" class. Testing on Caltech101 with training other domains. The x-axis shows sample selection ratios per class, with domain ratios above the bars. (Dark: clean samples; light: noisy.) The decline in ID and OOD performance balance increases suggests that more balanced distribution does not always improve OOD accuracy, and increased noise harms both ID and OOD, See Appendix A. for discussion.
separates samples into a clean and noisy set, and then aims to use the clean set to correct the noisy labels, which also the general approach taken by relabeling methods like DISC (Li et al., 2023) and UNICON (Karim et al., 2022). Similarly, FFng et al. (2023); Zhuo et al. (2023) aims to estimate label uncertainty and then re-weight samples based on this uncertainty, which also the general approach of LNL method PLM (Zaoo al. 2024). As seen in Table 9)), DISC, UNICON, and PLM underperform our DLAND approach by up to 18%.
Zhuo
The reason these methods do not transfer well is due, at least in part, to the differences in DA and DG, as there tends to only be a transfer between a single source domain and a single target domain. Specifically. if consider the example in Figure of our paper, the main problem NAG we must learn to separate label noise from domain shifts. However, in DA there is no need to solve this problem there is only single source domain with noise. Thus, they more akin the traditional LNL. task that aims to identify noise a single domain, which is likely why similar ideas appear between them. We simulate this type of setting by using per-domain sampling Table 7 effectively converting the problem into set of single-domain noise detection tasks. However, this underperforms our approach by 2% on VLCS. This not only highlights the differences between the tasks but also illustrates the benefits of our cross-domain comparisons.
raise is that in
C REAL-WORLD DATASET'S NOISE
Figure reports statisties on the two datasets used most extensively in our experiments, VLCS (Fang et al. 2013) and CHAMMI-CP (Chen et al.) 2024b). Generally speaking, CHAMMI-CP is larger, with more domains and is very noisy. However, while there are many domains, these represent what can be thought of as reproduction experiments. I.e., the variations that are shown are due to technical variations that arise when reprodueing an experiment within the same laboratory setting (i.e., same equipment). As such, their domains are more similar than those in VLCSS
Table provides more detailed statistics about the source of the noise in VLCS. Notably, the various domains have different degrees of noise, e.g., Caltech (Fei-Fei et al.) 2004) is relatively clean, whereas LabelMe (Russell et al. 2008) and SUN09 (Choi et al. 2010) are much noisier. The main source of the noise is due to unlabeled categories. For example, person images that do not label cars or chairs are common. As such, while it is the person images being labeled as noisy, the is also annotated. This makes this noise type similar to the asymmetric
actual issue is that noise used in our synthetic noise experiments in Section Our inspection of PACS (Li et al. 2017a) finds contains the same type of noise VLCCS.
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Dataset
VLCS
CHAMMI-CP
Task
Semantic Clessification
Treatment Classiftcation
Domains
REst. Noàse
30%
Over 50% for weak treatment
# Images
10,729
75,895
Weak
Label
Noise
Control
Dog
VIILY
D1
Shift
Chair
Chair
Figure 9: Real-world datasets with in-domain noise and multi-domain distribution. VLCS (web/user data) (Fang et al.] 2013), and CHAMMI-CP (biomedical images) (Chen et al. 2024b). VLCS faces label noise from poor annotations and domain shifts from varying data sources, while CHAMMI-CP deals with ambiguous features and varying experimental environments.
C.I SYNTHESIZED NOISE
Our synthetic experiments use asymmetric noise, which flips labels between commonly confused categories, These confusions can, but are not required to go both directions, and are manually selected. We provide our category confusion information in Table 15 for RotatedMNIST (Ghi-)
fary et al., 2015). Table 16 for OfficeHome (Venkateswara et al. 2017). Table 17 for Terralncog- nita (Beery et al. 2018). and Table 18 for DomainNet (Peng et al.
2019
D IMPELLENNATION DETAILS
We incorporate the implementation of the ERM+ Teterwaak al. 2025bb. DISC
Li al.
2023
UNICON
Karim et al. 2022).
ELR
(Lu et al. 2020). SAGM 6 Wang et al.
2023
MIRO
Cha et al. 2022), VREx Krueger et al. 2021) Fishr Rame al. 2022
DISAM 10
(Zhang et al. 2024), PLM I Zhao et al. 2024). into our codebase. Each training batch
includes samples from all training domains, with a batch size of 128. For relatively small datasets
VLCS (Fang et al. 2013) and CHAMMI-CP (Chen al. 2024b), experiments are run single NVIDIA RTX A6000 (48GB RAM) and three Intel(R) Xeon(R) Gold 6226R CPU 2.90GHz for 5000 steps. Below give short description of the methods in prior work used in our experiments.
D.1 LEARNING WITH NOISY LABELS METHODS
ELR (Liu et al., 2020) is based on the observation that deep neural networks initially fit the training data with clean labels during an "early learning" phase before eventually memorizing examples with false labels. It employs semi-supervised learning to generate target probabilities and introduces a regularization term to prevent memorizing false labels, guiding the model toward these target probabilities.
hantss//ghhhbb.comm/iorttttwee/ plusplus
https//giitubbboom//eeX////LD
hutp://gthh...mmmmammmmmaaiantto https///gttubbbomm//eeeggii////f
hhtt:://iittalcccm///agggggggggg
http:///tthaaccmmm/ccaiiaassssss 10 http:///ggthiicccm///////ottttte
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Table 14: VLCS Dataset Overview (Total Samples, Noisy Samples) over its datasets Caltech (Fei-
Fei et al. 2004), LabelMe (Russell et al. 2008), SUN09 (Choi et al. 2010), and VOC2007 Ever-
ingham et 2010).
Domain
Category Bird
Total Samples
Noisy Samples
237
(with person)
Caltech
Car
123
0
(black & white car imgs)
Chair
118
0
Dog
67
0
(only black and white dog)
Person
870
0
(profile photos with redundancy)
Bird
80
20
Car
1209
559
LabelMe
(background: building, road, mountains;
small & incomplete cars, unclear night imgs [OOD))
Chair
89
61
(over half have cars, person)
Dog
43
25
(with person, cars)
Person
1238
924
(over 80% noisy images have cars,
street photos are similar to car and chair categories, small person figures)
Bird
12
(background, I person and dog)
SUN09
Car
933
548
(street view, buildings, person)
Chair
1036
186
(mostly person, very few car interior)
Dog
31
25
(~~20 noisy images with person)
Person
1265
631
(very small person figures)
Bird
330
29
(mostly human, a few cars, one small bird)
VOC2007
Car
699
133
(mostly person, ~ don't have cars)
Chair
428
145
(mostly person, some cars, very few missing chair)
Dog
420
11I
(mostly human, a few cars)
Person
1499
61
(mostly cars, some don't ton't have person)
partitions the training set into clean and noisy subsets using uniform selection at each iteration. estimates the clean label probability via Jensen-Shannon divergence (JSD) based prediction and one-hot label distribution disagreement. After partitioning, UNI- CON applies semi-supervised learning (SSL) with a contrastive loss across two identical networks, repeating this process until convergence.
UNICON (Karim al. (022)
DISC Li et al. 2023) identifies clean samples based on predictions from weak and strong augmen- tations, using dynamic confidence threshold determined by each instance's 's memorization strength from previous epochs. This approach classifies instances into clean, hard, and purified subsets. Different regularization strategies then applied to each subgroup.
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Table 15: Asymmetric Noise Pairs for RotatedMNIST
Ghifary et al. 2015).
Index A
Index B
0
6
1
3
5
4
9
5
3
6
0
7
1
9
4
Table 16: Asymmetric Noise Pairs for OfficeHome
Venkateswara et al.
2017
Index
Class A
Index B
Class
16
Pencil
6
Pen
14
Keyboard
42
Laptop
15
Mouse
60
Monitor
Backpack
39
Cliphoards
Calculator
34
Notebook
47
Bottle
63
Soda
13
Flowers
21
Candles
Flipflops
54
Sneakers
TV
60
Monitor
Speaker
53
Radio
4
Kettle
52
Pan
19
Wehcam
42
Laptop
Mop
56
Bucket
24
Knives
32
Fork
Desk Lamp
33
Lamp Shade
18
Spoon
32
Fork
Scissors
27
Screwdriver
50
Hammer
22
Drill
48
Computer
60
Monitor
23
Folder
34
Notebook
26
Post-it Notes
61
Paper Clip
58
File Cabinet
36
Shelf
44
Push Pin
26
Post-it Notes
45
Sink
62
Refrigerator
49
Fan
33
Lamp Shade
25
Mug
47
Bottle
57
Couch
30
Chair
Table 17: Asymmetric Noise Pairs for Terralncognita (Beery et al. 2018).
Index A
Class A
Index B
Class B
0
Bird
9
Squirrel
1
Bobcat
3
Coyote
2
Cat
4
Dog
3
Coyote
8
Raccoon
4
Dog
2
Cat
5
Empty
0
Bird
6
Opossum
8
Raccoon
7
Rabbit
9
Squirrel
8
Raccoon
6
Opossum
9
Squirrel
7
Rabbit
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Table 18: Asymmetric Noise Pairs for DomainNet
Peng et al. 2019
Key
Value
Key
Value
Key
Value
Key
Value
Key
Value
Key
Value
308
208
2
135
5
0
7
324
8
324
208
10
288
11
324
12
208
13
285
14
208
15
16
16
17
17
282
18
19
19
327
20
309
21
208
22
327
23
208
288
135
26
28
208
29
327
31
98
32
34
35
36
282
37
38
38
327
39
208
40
208
41
42
42
208
43
44
44
308
45
46
46
331
47
324
48
91
49
90
50
327
51
324
52
53
53
324
327
56
151
334
59
60
324
61
208
62
175
63
64
64
327
65
208
66
67
67
68
68
208
69
208
70
138
71
331
72
324
73
175
74
53
75
254
76
338
77
276
91
79
208
80
208
282
83
319
85
86
208
89
90
91
91
208
92
323
93
285
94
95
95
261
96
276
97
98
98
324
99
282
100
288
101
102
102
103
103
327
104
110
105
288
106
107
107
282
276
110
110
288
113
114
157
115
208
116
327
117
98
118
327
119
208
120
208
121
110
122
324
123
208
124
125
125
208
126
208
127
324
128
129
129
208
130
327
131
208
132
208
133
28
134
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24

---

Published as a conference paper at ICLR 2026
PLM (Zhao et al. 2024) is classifier-consiseen method, which estimates the noisy class posterior with noise transition matrix to correct the label in the training. PLM crops instances to generate part- level labels, which are then modeled with novel kinle-to-multipe transition matrix to capture the relationship between noisy and part-level labels.
D.1.1 DOMAIN GENERALIZATION METHODS
ERM (Gulrajani Lapez-Paz 2021a) is the simplest baseline method, where models are simply trained on the multiple sources.
ERM++ (Teterwak et al., 2025b) an enhanced baseline that tunes training components to mitigate overfitting and boost generalization performance. It employs a two-stage training pipeline, explores strong initialization strategies, and investigates regularization techniques such as Model Parameter Averaging, Warm Start, Unfreezing BatchNorm, and Attention Tuning.
MIRO Cha et al. 2022 is a DG framework that guides learning by maximizing mutual informa- tion between oracle representations, approximated with pre-trained models. The objective function combines empirical risk and a variational bound of the mutual information, effectively enhancing generalization ability.
SWAD CCaa et al. 2021) improves DG performance by finding flat minima in the loss landscape. extends SWA with dense sampling strategy and an overfit-aware sampling schedule, resulting in flatter minima and better generalization across domains while being robust to model selection and overfitting issues.
Fishr (Rame et al. 2022) is a regularization method that enforces domain invariance by matching the domain-level variances gradients across training domains. By aligning the domain-leved loss landscapes around the final weights, Fisher effectively improves out- out-of- distriibutiio generalization.
VREx (Krueger et al.) 202T method that aims reduce differences risk across training domains to enhance robustness against extreme distributional shifts, with the assumption that domain shift is type of variation. It proposes a penalty on the variance training risks.
SAGM (Wang et al. (023) boosts generalization across domains by finding flat regions with low loss. minimizes the empirical risk loss, perturbed loss, and surrogate gap while performing gradi- ent matching, efficiently guiding the model towards flat, low-loss regions, resulting in better gener- alization compared to SAM-like methods.
DISAM (hhng et al.) 2024) addresses domain-level convergence consistency in sharpness-aware minimization (SAM) by introducing a constraint to minimize variance in domain losses. This approach prevents excessive insufficient perturbations in domains that less more well- optimized.
D.2 DETAILS ON DG+LNL INTEGRATION
Algorithm show the detail of the integrated methods.
25

---

Published as a conference paper at ICLR 2026
Input Sample inputs X = {-;) noisy labels Y [i ELR temporal ensembling
momentum B, regularization parameter X, neural network with trainable parameters fo Output: Neural network with updated parameters for
for step &- 1 to training-sleps do
for minibatch B do for in B do
Pt fo(a;) /// Model prediction. 4 #4 + (1 B) Pp:// Temporal ensembling
end
loss = B E c1pcrross.enlropp vi) [8] L log(1- < 44 ELR
loss: cross entropy loss and regularization loss.
Update fo.
end for
Update fo with ERM++ parameter averaging.
end
Algorithm 1: ERM++ + ELR Algorithm.
Input Sample inputs X = {-;};_. noisy labels Ý = ( 1 ELR temporal ensembling
momentum 8, ELR regularization parameter X1, MIRO regularization parameter X2
MIRO mean encoder μ4, MIRO variance encode a,, feature extractor with trainable parameters fa. pretrained feature extractor with parameters fa,
Output: Neural network with updated parameters for for step + 1 to training.steps do
for minibatch B do for i in B do
Pt fo(x:) // feature extractor output.
p. fa, (zx) / Pretrained feature extractor output.
4 3 444 + (1,+((11))pp//// Temporal ensembling.
+3)p/://
end
loss
B cincrrosscntrrrpppp........) // Cross entropy loss.
loss
Al += 13 1o < p.,
>): < ELR loss with regularization
term.
loss | E (log((a(p))) + 1l ((xi)ll MIRO loss with
regularization term.
Update fg.
end
for
Updated fe.
end
Algorithm MIRO ELR Algorithm.
26

---

Published as a conference paper at ICLR 2026
Input Sample inputs X (a.)ī=1. noisy labels Y { ELR temporal ensembling
momentum B. ELR regularization parameter X, neural network with trainable parameters fo
Output: Neural network with updated parameters for for step f- 1 to training sleps do
for minibatch B do for i in B do
Pt fo(z)) Model prediction.
4 = 3+ (1-3)))1) Temporal ensembling.
B/:p:///
end
loss = [ Piggcroosscmtrropppp vi) [8] L 10g(1- < P:, >):// ELR
loss: cross entropy loss and reqularization loss.
Update fo. Decide the start slep, and end slepe iteration for averaging in SWAD.
end
for
step. step, +1 Efo:/ SWAD parameter averaging.
end
Algorithm 3: SWAD + ELR Algorithm.
Input Sample inputs X = {x; ( noisy labels Ý [ ELR temporal ensembling
momentum B, ELR regularization parameter A1, MIRO regularization parameter X2, MIRO mean encoder μu, MIRO variance encode or, feature extractor with trainable parameters for pretrained feature extractor with parameters fa. Output: Neural network with updated parameters for for step + to training.steps do for minibatch B do for i in B do
Pt fo(x.) /// feature extractor output.
p. fo, (zx) :// Pretrained feature extractor output.
f; +t, + (1- 11=3))p;:////// Temporal ensembling.
end
loss
[ [nnprrross.ttrrooyoo..::::: Cross entropy loss.
loss += Al [
>):// ELR loss with reqularization
loss +=
X2
m (log((((P)) + |l p(p))l //:// MIRO loss with
[8]
reqularization term.
Update fg. Decide the start step, and end step. iteration for averaging in SWAD.
end
f0r
step, step.tI EJo // SWAD parameter averaging.
end
Algorithm 4: MIRO SWAD + ELR Algorithm.
27

---

Published as a conference paper at ICLR 2026
Input Sample imputs X {2.))==> noisy labels y = {ir 1i
MIRO regularization
parameter X2, MIRO mean encoder MIRO variance encode feature extractor-1 with trainable parameters fl,, feature extractor-2 with trainable parameters /22,, pretrained feature extractor with parameters f&g. UNICON sharpening temperature T, UNICON unsupervised loss coefficient Au. UNICON contrastive loss coefficient Åo.
UNICON regularization loss coefficient X
Output: Neural network with updated parameters flo' and f220'
for step 1 to training _stppsseepppeee do
Delann Daoisy UNICON Selection(X = 1 f20),; UNICON
clean-noist sample selection.
for clean minibatch Beean do for noisy minibatch Broisy do for in B = Belean Unnsss do
pl: flo(x:): :// feature extractor-1 output.
p2: = f2o(zz); ;// feature extractor-2 output.
p. [No(x)): // Pretrained feature extractor output.
end
loss] |8 pngeossstetrrrppp):...... Cross entropy loss for
feature extracrr-1
loss1+ = 8 (Iog((( + Ip? - p(pl))ll MIRO loss
x2
with regularization term for feature extractor-1.
loss2 T8 > cross 2wt.ossseerrooopp22...... 9:) Cross entropy loss for feature extractor-2,
loss.+ = X2
88 n (1og((((222)) + 1p u(p2,))l 1);// MIRO loss
with reqularization term for feature extractor-2.
Xweak
clean| B weak-augmentation/ Betean)
X wwekk
noisyl weak-augmentatton// (nninnenntattio(nBnne)))))
X strong
clean[B] strong-augmeenttiion/ (Betea))
x ystrong = langgg-ammmtatiiBBB.... (Booisy))
noisu] BI
Get labeled set with UNICON label refinement clean batch.
Get unlabeled set with UNICON pseudo label on noisy batch.
Lui, L, MixMatch labeled and unlabeled sets: // UNICON
unsupervised loss for feature extractor-1 and extractor-2.
Get Lei //// UNICON contrastive loss for feature
extractor-1 and extractor-2.
Get L,1. UNICON regularization loss for feature extractor-1 and extractor-2.
loss = > Lu + La+t, L/1/// Update UNICON loss for feature extracter-1,
1oss2+ A Lu2 + d La + Xr L/2:// Update UNICON loss for feature extracte--2 Update flo and f2g.
end end
fl = Updated fla. f2a Updated f2g.
end
Algorithm 5: MIRO UNICON Algorithm.
28

---

with regularizatin term for feature extractor-l. Cross entropy loss for Published as a conference paper at ICLR 2026
Input Sample imputs X
{..)²=1. noisy labels Y =
f= MIRO regularization
parameter X2, MIRO mean encoder MIRO variance encode a, feature extractor-1 with trainable parameters fla, feature extractor-2 with trainable parameters f20, pretrained feature extractor with parameters feo. UNICON sharpening temperature T, UNICON unsupervised loss coefficient Au, UNICON contrastive loss coefficient λ..
UNICON regularization loss coefficient X
Output: Neural network with updated parameters fla and f2
for step to training steps do
Delann Daoisy = UNICON Selection(X 1 f20),; UNICON
clean-noisy sample selection.
for clean minibatch Betan do for noisy minibatch Buoisy do for in B Betean Unoisy do feature extractor-1 output. p2, = f2o(2,); ;// feature extractor-2 output.
pl: flo(z;); ;/
P. (0o( Pretrained feature extractor output.
end
loss] B Brosss x)) Cross entropy loss for
feature extractor-1.
loss = x2
[ D (log((((pp)))) + lpf - p(pl)) -(pl))llp))) MIRO loss
loss2 nn cross feature extractor-2.
loss2+ = X2
[8 D (100(((2222) + 1I ((p2i)ll MIRO loss
with regularization term for feature extractor-2.
Xweek
clean B| weak- augaeentggmttiionn Betean)
X weak
weak-augmentation/ Bnoiss)
noisyi B
X strong
clean[ [B] arrong-augmennationn Belean)
X strong
noisy = stoog-auggmennaiionnnBooooo
Get labeled set with UNICON label refinement clean batch.
Get unlabeled set with UNICON pseudo label on noisy batch.
L.i MixMatch labeled and unlabeled sets // UNICON
unsupervised loss for feature extractor-1 and extractor-2.
Get Le, UNICON contrastive loss for feature extractor-1 and extractor-2.
Get Lr1, UNICON regularization loss for feature extractor-1 and extractor-2.
(oss++= Lu1 + + Le1 + X, L,1: /// Update UNICON loss for feature extracter-.
loss2+= X Lu + X T,2 + X, T, /// Update UNICON loss for feature rxttocr---
Update fla and f2g. Decide the start step, and end step. iteration for averaging in SWAD.
end end
fla step step. averaging. end
SWAD parameter Efle f2x step. step, +1 Ef2a
Algorithm 6: MIRO SWAD UNICON Algorithm.
29


Reviews:
[{"review_id": "mTuAbOLM5Z", "reviewer": "Reviewer_fTkm", "summary": "This paper studies the joint problem of robustness to label noise and domain shifts, termed Noise-Aware Generalization (NAG). The authors propose DL4ND, a method that detects noisy samples by performing cross-domain comparisons among low-loss examples. The idea is that comparing samples across domains helps identify intrinsic class features and avoid confusion caused by domain-specific artifacts. Experiments on several benchmark datasets demonstrate that DL4ND improves both in-domain and out-of-domain performance over prior LNL and DG methods.", "questions": "- Have the authors evaluated whether low-loss samples truly correspond to clean labels in practice?\n    \n- How robust and reliable is the clean/noisy distinction under different training dynamics or noise levels?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- The paper is easy to follow and logically developed.\n    \n- The proposed approach is reasonable and intuitively well-motivated, combining insights from both LNL and DG.\n    \n- Extensive experiments across multiple datasets validate the effectiveness of the method and provide detailed ablation analysis.", "weaknesses": "- Although the setup is novel, it appears somewhat artificial, and its practical real-world scenarios are unclear.\n    \n- The method heavily relies on the multi-domain assumption. It is uncertain how DL4ND would perform if only one source domain with noisy labels were available.\n    \n- The analysis in this manuscript (such as lines 251–258) is mostly heuristic and lacks rigorous theoretical justification or stronger empirical evidence/observation.\n    \n- The experimental evaluation could be further strengthened, for example by involving more diverse noise types.", "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": "fk8kupV20f", "reviewer": "Reviewer_SNwE", "summary": "Learning with Noisy Labels (LNL) addresses label noise and multi-source Domain Generalization (DG) handles domain shifts to boost downstream performance, but prior work mostly explores them in isolation, with limited efforts to mitigate label noise’s impact on DG.\nMany applications require robustness to both label noise and distribution shifts (defined as Noise-Aware Generalization, NAG), posing challenges: LNL’s assumption that distribution shifts equal label noise fails, and DG’s neglect of label noise harms training. A naive NAG solution uses domain labels to separate shifts but wastes cross-domain information, while the proposed DL4ND improves noise detection by leveraging greater variation of noisy samples across domains. Experiments on seven diverse datasets show DL4ND significantly enhances performance.", "questions": "Please refer to the Weakness.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. This paper is easy to follow.\n\n2. The performance of the proposed method is good. The experimentail results are relatively extensive.", "weaknesses": "1. It seems that there is no practical application of NAG in real world, so is it meaningful to address this new setting? The authors should discuss the potential value in real world application.\n\n2. More related methods [1-4] in the filed of Robust Domain Adaptation under Label Noise should be reviewed and discussed. \n\n[1] Y. Shu, Z. Cao, M. Long, and J. Wang, “Transferable curriculum for weakly-supervised domain adaptation,” in Proc. AAAI Conf. Artif. Intell., 2019, vol. 33, pp. 4951–4958.\n\n[2] Z. Han, X. Gui, C. Cui, and Y. Yin, “Towards accurate and robust domain adaptation under noisy environments,” in Proc. 29th Int. Joint Conf. Artif. Intell., C. Bessiere, Ed., 2020, pp. 2269–2276. \n\n[3] Y. Zuo, H. Yao, L. Zhuang, and C. Xu, “Seek common ground while reserving differences: A model-agnostic module for noisy domain adaptation,” IEEE Trans. Multimedia, vol. 24, pp. 1020–1030, 2022. \n\n[4] Junbao Zhuo, Shuhui Wang, Qingming Huang. Uncertainty modeling for robust domain adaptation under noisy environments. IEEE Transactions on Multimedia. pp. 6157-6170. 2023.", "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": "PccaFknjOm", "reviewer": "Reviewer_wQm5", "summary": "The paper studies the joint setting where data contains both domain shifts and label noise, which the authors call Noise-Aware Generalization. The idea is that existing domain generalization and noisy-label learning methods usually handle one problem at a time. The proposed method, DL4ND, uses cross-domain feature comparison to detect mislabeled samples. After a warm-up stage, it selects low-loss samples as clean, computes class-domain prototypes, and relabels high-loss samples based on the closest prototype from another domain. The method can be combined with standard DG approaches such as ERM++, SWAD, or SAGM. Experiments on seven datasets show consistent but relatively modest improvements.", "questions": "1. How does the proposed cross-domain comparison differ fundamentally from prototype-based noisy-label methods such as DivideMix or UNICON?  \n2. What motivates defining Noise-Aware Generalization as a new task rather than treating it as DG with label noise?  \n3. Can you provide visualization or analysis to support the claim that cross-domain comparison improves noise detection?  \n4. How sensitive are results to the relabeling frequency and the accuracy of domain labels?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The topic is relevant and realistic since many real-world datasets contain both domain shifts and noisy annotations.  \n2. The method is simple and easy to integrate into existing frameworks.  \n3. Experimental coverage is broad, with multiple datasets and several DG and LNL baselines.  \n4. The paper is well written and figures are clear.", "weaknesses": "1. The novelty is limited. The proposed approach mainly reuses existing ideas from noisy-label learning such as loss-based sample filtering, GMM separation, and prototype relabeling. The only new component is comparing features across domains, which is a small modification conceptually.  \n2. The framing of Noise-Aware Generalization as a new task feels overstated, since similar scenarios have appeared in prior DG or LNL discussions.  \n3. The paper lacks deeper analysis or theory explaining why cross-domain comparison works better.", "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": "peJzjvKRJS", "reviewer": "Reviewer_QyAy", "summary": "This paper introduces Noise-Aware Generalization (NAG) to tackle noisy, diverse real-world data by improving in-domain noise handling and out-of-domain generalization. The authors show that distinguishing noise from domain shifts is challenging, and naive combinations of LNL and DG fail because domain shifts interfere with noise detection. They propose using cross-domain comparisons as a signal for identifying noise, leveraging its lack of intrinsic class features. Experiments validate that this approach significantly improves performance and offer insights for further advancing NAG.", "questions": "Please refer to the strengths and weaknesses of the paper.", "limitations": "", "rating": 8, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "1. The paper is well-motivated and seems to be reproducible.\n2. This paper is well-organized and easy to follow. Specifically, in Sce. 3, the authors provide an in-depth analysis of the causes and implications of the proposed task, and in Sec. 4, they present concrete solutions, which are highly insightful.\n3. This paper addresses real-world challenges and significantly improves task performance.", "weaknesses": "1. Existing works [A][B] have discussed the presence of real-world noisy labels in domain adaptation, which this paper seems to overlook. The related work section should be improved to include comparative discussion with these methods.\n2. The formula proposed in Section 3.1 is explored with a toy experiment in Section 3.2. In my view, the authors could provide deeper theoretical analysis to make the argument more convincing.\n3. The experiments lack some qualitative analysis to illustrate specific cases, which would help demonstrate the method’s advantages and enhance understanding of the task.\n4. Currently, NAG is evaluated on synthetic noisy data. Introducing more realistic “asymmetric noise,” as studied in noisy label learning, could improve the practical applicability of NAG.\n5. In my opinion, the abstract focuses almost entirely on the NAG task itself, while neglecting to highlight the insightful ideas behind the proposed method.\n\n[A] Feng, Yanglin, et al. \"ROAD: Robust unsupervised domain adaptation with noisy labels.\" Proceedings of the 31st ACM international conference on multimedia. 2023.\n\n[B] Yin, Ziniu, et al. \"RoDA: Robust Domain Alignment for Cross-Domain Retrieval Against Label Noise.\" Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 39. No. 9. 2025.", "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)