Pith. sign in

REVIEW 5 major objections 5 minor 102 references

Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation

T0 review · 5 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read The paper argues that explicit gradient diversity improves weight-averaged out-of-distribution generalization, and that weight averaging plus SAM substantially boosts few-shot domain adaptation.

desk verdict An honest but incremental thesis whose central OOD claim is not supported by its own tables; the few-shot DA results are more interesting but lack a no-WA control. read the letter →

arxiv 2501.08361 v1 pith:CUA3P5OQ submitted 2025-01-14 cs.CV cs.LG

Shijian Xu This is my paper · ORCID
classification cs.CVcs.LG
keywords weightaveragingout-of-distributiongeneralizationfew-shotdomainadaptationgradientsimilaritysharpness-awareminimizationmodeldiversitycovariateshift
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper sets out to strengthen weight averaging as a tool for distribution shift. Its first claim is that explicitly encouraging diversity among the models being averaged — by adding a cosine-similarity penalty on their gradients — improves out-of-distribution generalization over plain weight averaging. Its second claim is that combining weight averaging with sharpness-aware minimization (SAM) gives large gains in few-shot domain adaptation, particularly when the averaged model is adapted to the target domain with a few labeled examples. The experiments cover in-distribution CIFAR100, the public OOD benchmarks PACS and VLCS, digit datasets (MNIST, SVHN, USPS, MNIST-M), and VisDA-C, and the reported gains are largest on the digit adaptation tasks. If the claims hold, practitioners get a way to make averaged models more transferable without any extra cost at inference time.

What carries the argument

The central machinery is the gradient-similarity regularizer: for each pair of models being trained for averaging, the loss of each model gains the term $\mathrm{cossim}(\nabla_{h_{k1}} f_{\theta_{k1}}(x), \nabla_{h_{k2}} f_{\theta_{k2}}(x))$, the cosine similarity between the two models' gradients with respect to their shared feature layers, which the paper uses to push the paired models toward different solutions before their weights are averaged. The other load-bearing ingredient is the weight-averaging protocol itself: all models start from a shared initialization (an ImageNet-pretrained ResNet-50 with a linear-probed final layer, or a CNN trained to at least 85% accuracy on the source domain), each run uses a different combination of learning rate, weight decay, SAM radius, and dropout drawn from a small random search, and the final model is the uniform average of all weights. For few-shot adaptation, the pipeline adds a fine-tuning step on $k$ samples per class of the target domain, and the paper finds that fine-tuning the already-averaged model works better than fine-tuning each model before averaging.

What would settle it

Train the same paired models with a sweep of the gradient-similarity penalty weight and measure both the cosine angle between the final weight vectors and the averaged model's OOD accuracy; if accuracy does not rise as weight-space angle grows, the diversity mechanism is not what drives the gain. A matched-model-count comparison of WA+grad against DiWA on PACS and VLCS (using 60 averaged models with identical hyperparameter ranges) would also settle whether the regularizer adds anything beyond plain weight averaging.

Watch

Extended reading notes

Core claim

On the paper's own terms, the discovery is that weight averaging's gains can be driven by explicit model diversity rather than only by the number of models averaged. The OOD method trains pairs of models from a shared initialization and adds the cosine similarity between the pair's gradients to each model's classification loss; averaging all trained weights then yields a model that the paper reports as competitive with prior weight-averaging baselines on PACS and VLCS. For few-shot domain adaptation, the paper shows that averaging source-trained models and then fine-tuning the averaged model on k labeled target examples per class outperforms adapting each model before averaging, and that using SAM as the optimizer yields the better results on the digit benchmarks. On the digits datasets the averaged pipeline exceeds the FADA, CCSA, and d-SNE baselines on several transfer tasks, while on VisDA-C it falls well short of those baselines; the paper identifies this gap as an open issue for further analysis.

Load-bearing premise

The method assumes that forcing paired models to have dissimilar gradients will push their learned weights into positions where the averaged model is more accurate on out-of-distribution data, rather than simply making each individual model worse.

Editorial extensions

If this is right

  • If the gradient-similarity mechanism works, then explicit diversity regularization becomes a lever for improving weight-averaged models under covariate shift, complementing the implicit diversity from hyperparameter variation.
  • If WA+SAM is as effective as reported on digits, few-shot domain adaptation can be done by averaging a handful of source models and fine-tuning the average, avoiding the need to fine-tune each model separately.
  • The finding that adaptation after averaging beats adaptation before averaging implies that preserving the common features learned by the ensemble is critical; averaged models are a better starting point for target fine-tuning than any single adapted model.
  • The ablation showing diminishing returns beyond roughly 20 averaged models suggests that moderate compute budgets suffice to capture most of the OOD gain.
  • The paper's in-distribution CIFAR100 results imply that the same recipe helps standard i.i.d. accuracy, so the method is not only a distribution-shift tool.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • One reading of the reported numbers is that the gradient-similarity penalty is not yet the right diversity mechanism: WA+grad's averages on PACS and VLCS sit slightly below DiWA's with far fewer models, and Section 6 itself concedes the results were 'not entirely promising.' A testable alternative is to regularize on the weight-space angle directly, or to anneal the penalty during training.
  • The large VisDA-C gap (49.41 vs 80.66 for d-SNE) suggests that WA+fine-tuning may need a stronger adaptation step for large domain gaps; applying the gradient-diversity regularizer during the adaptation phase, rather than only during source training, is a natural next experiment.
  • Because the strongest few-shot gains appear on digit-to-digit transfer, the recipe may be a cheap and strong baseline for small-scale domain adaptation; comparing WA+SAM against self-supervised or feature-alignment methods on the same tasks would clarify where the gain comes from.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 5 minor

Summary. This manuscript, originally an MSc thesis, studies weight averaging for out-of-distribution (OOD) generalization and few-shot domain adaptation. It proposes two extensions: (i) a gradient-similarity regularizer applied to pairs of models during fine-tuning, intended to increase model diversity before weight averaging, and (ii) combining weight averaging with sharpness-aware minimization (SAM) for few-shot domain adaptation, with adaptation performed either before or after averaging. Experiments are reported on CIFAR-100, PACS, VLCS, digit datasets (MNIST, SVHN, USPS, MNIST-M), and VisDA-C, with comparisons to ERM, DiWA, SWAD, MA, Coral, and several few-shot DA baselines. The abstract claims improved OOD generalization from gradient-diversity regularization and significantly improved few-shot domain adaptation accuracy from combining WA and SAM.

Significance. If the central claims were substantiated, the work would offer a simple training-time regularizer for improving weight averaging under distribution shift and a practical few-shot domain adaptation recipe. The manuscript is transparent about its limitations and contains algorithm listings, ablation studies, and public-benchmark evaluations. However, the reported evidence is mixed or negative on the decisive comparisons: the proposed WA+grad is below DiWA on the average over both DomainBed datasets, the VisDA-C result is far below two baselines, and the few-shot DA experiments lack a no-weight-averaging control under the same setup. The main empirical claims are therefore not established, and the manuscript would need substantial new experiments and a substantially more modest claim before it could be considered for publication.

major comments (5)
  1. [§5.1, Tables 4 and 5] The headline OOD claim is not supported by the paper's own tables. WA+grad obtains 88.46 on PACS with M=40 and 77.86 on VLCS with M=30, while DiWA with uniform selection and M=60 obtains 89.0 and 78.6, respectively. The comparison against DiWA restricted (M≤20) is also mixed: 88.46 vs 88.0 on PACS, but 77.86 vs 78.5 on VLCS. No standard deviations or significance tests are reported for the DiWA uniform or WA+grad numbers, and the number of averaged models differs. Matched comparisons with equal M, equal training budget, and multiple seeds are needed before any improvement over DiWA can be claimed.
  2. [§4.2, Algorithm 1] The mechanism underlying the proposed regularizer is not verified. The paper never reports the gradient cosine-similarity values, the resulting pairwise weight angles, or an ablation that isolates the gradient regularizer from the choice of optimizer and hyperparameters. Without such measurements, the claim that penalizing gradient similarity increases weight-space diversity and thereby improves the averaged model's OOD accuracy remains an unverified hypothesis. In addition, cossim is not formally defined in Algorithm 1, and no regularization coefficient is specified, so the procedure is not fully reproducible as written.
  3. [§5.2, Tables 6–8] The few-shot domain adaptation experiments lack a no-weight-averaging control under the same training setup. The comparisons to FADA, CCSA, and d-SNE use different architectures, backbones, and experimental protocols, so the reported gains cannot be attributed to weight averaging. The optimizer ablation in Table 8 compares Adam versus SAM but always within weight averaging; it does not compare WA to single-model fine-tuning. Adding a single-model baseline fine-tuned with the same optimizer, backbone, and adaptation protocol is necessary to isolate the effect of weight averaging.
  4. [§5.2, Table 9 and §6] The VisDA-C result directly contradicts the abstract's claim that combining WA and SAM significantly increases few-shot domain adaptation accuracy. Table 9 reports WA at 49.41 versus 80.66 for d-SNE and 79.2 for LCCS, and the text describes this as a 'significant discrepancy' with additional analysis promised. The Discussion also states that the gradient-diversity experiments were 'not entirely promising.' These are explicit limitations in the manuscript itself, and they undermine both central claims as stated.
  5. [§2.4 and §5.1] The proposed gradient-similarity regularizer is closely related to the ensemble-diversity method of Teney et al. [85], which is cited in Related Work but not included as a baseline or discussed in the experiments. The manuscript should explain the distinction between enforcing diversity in prediction ensembles and enforcing it in weight-averaged models, and it should compare against [85] empirically. As written, the claimed novelty of explicitly training models with a gradient-similarity regularizer for diversity is substantially weakened by this omission.
minor comments (5)
  1. [§3.6, Eq. (17)] The condition 'where ρ≤0' should read ρ≥0 for the SAM neighborhood size; as printed, the constraint is impossible.
  2. [§3.6, Eq. (20)] The right-hand side of the SAM update approximation omits the learning rate λ that appears on the left-hand side; the notation should be made consistent.
  3. [Table 2] The dropout distribution entry '[0, 0.1l 0.5]' contains an apparent typo ('l' instead of a separator or bracket) and should be corrected.
  4. [Throughout] There are multiple typos, including 'nueral' in §3.2, 'covraiate' in §3.2, 'problmes' in §2.6, and 'tacked' in §6; a careful proofread is needed.
  5. [Table 6] The note that the best 7-shot results are underlined and best 10-shot results are bold is not fully consistent: for several columns the best value is not uniquely identifiable, and no standard deviations are reported for the WA rows, making the comparisons hard to assess.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the proposed gradient-similarity regularizer and WA+SAM combinations are evaluated against external benchmark accuracies, and the paper's own discussion candidly reports mixed or negative results.

full rationale

This paper reports a training-time regularizer (Algorithm 1: loss_k1 ← ℓ(fθk1(x), y) + cossim(∇hk1 fθk1(x), ∇hk2 fθk2(x))) followed by uniform weight averaging, and two standard few-shot domain-adaptation protocols (Algorithm 2: average then adapt; Algorithm 3: adapt then average). The claimed outcomes are test accuracies on CIFAR100, PACS, VLCS, digit-transfer, and VisDA-C, all external to the regularizer; no quantity in the regularizer is fitted to those accuracies, and no reported 'prediction' is algebraically identical to a fitted input. The shared-initialization and mild-hyperparameter-search requirements are imported from independent prior work (DiWA, Model Soups), not from a self-citation chain, and they do not by construction force the reported improvements. The related-work section explicitly credits [85] for the gradient-similarity idea in ensembles, so the OOD regularizer is presented as an extension rather than a disguised known result. The only passages that could be read as undermining the claims are the Discussion's admission that the diversity experiments 'were not entirely promising' and the VisDA-C statement that 'Our current methodology exhibits a significant discrepancy when compared to state-of-the-art techniques'; these are explicit limitations, not hidden circular reductions. No self-citation is load-bearing ([94] and [95] are not by the thesis author and are cited only as background on extrapolation). For these reasons the derivation chain is self-contained against external benchmarks and the circularity score is 0.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The paper introduces no new entities. The central empirical claims rest on assumptions about linear mode connectivity, the angle-accuracy correlation from Model Soups, the proxy validity of gradient diversity, and the transferability of SAM flatness through averaging. The gradient-similarity regularization weight is a key unstated free parameter.

free parameters (3)
  • Gradient similarity regularization coefficient = unspecified (implicitly 1.0 in Algorithm 1)
    The loss in Algorithm 1 adds cossim directly to the classification loss without a tunable weight; the scale of this term is never stated, yet it determines the strength of the diversity pressure.
  • Number of averaged models M = 10 (CIFAR100), 40 (PACS), 30 (VLCS), 10 runs (digits)
    Chosen by hand per experiment; the ablation shows performance saturates around 20, but the main tables use different M, making cross-experiment comparison inconsistent.
  • Shared initialization accuracy threshold = 85% (digits CNN)
    Section 5.2 requires the shared init model to reach at least 85% accuracy on the source domain; the exact stopping point and whether the threshold affects averaging quality is not analyzed.
assumptions (4)
  • domain assumption Linear averaging of weights from a shared initialization retains low loss (linear mode connectivity).
    Invoked in Section 4.1, citing Frankle et al. [23] and Neyshabur et al. [66]; if this fails, weight averaging degrades.
  • domain assumption Larger angles between individual models yield greater accuracy gain for the averaged model.
    Motivation from Figure 1 (Model Soups [93]); the gradient diversity regularizer is designed to produce such angles. The paper does not verify the angle-gain correlation in its own runs.
  • ad hoc to paper Gradient similarity between pairs of models is a valid proxy for model diversity in weight space.
    Section 4.2 defines the regularizer on gradients of the penultimate layer h; no evidence is given that gradient diversity transfers to weight-space angle diversity.
  • domain assumption SAM finds flat minima that generalize better under shift, and this property persists after weight averaging.
    Section 3.6 cites Foret et al. [22]; Table 8 shows SAM helps on most rows, but the interaction with WA is not isolated.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation." pith.science (2026). https://pith.science/paper/CUA3P5OQ

@misc{pith2026250108361,
  author       = {Pith},
  title        = {Pith review of: Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CUA3P5OQ}},
  note         = {Machine review of arXiv:2501.08361}
}
read the original abstract

Empirical risk minimization (ERM) is not robust to changes in the distribution of data. When the distribution of test data is different from that of training data, the problem is known as out-of-distribution generalization. Recently, two techniques have been developed for addressing out-of-distribution generalization in computer vision: weight averaging (WA) and sharpness-aware minimization (SAM). WA involves training multiple models with different hyperparameters and then averaging the weights of these models, which can significantly improve out-of-distribution generalization performance. SAM optimizes a neural network to find minima in flat regions, which have been proven to perform well under distribution shifts. While these techniques have made great progress, there is still room for improvement and further exploration. In this thesis, we propose increasing the model diversity in WA explicitly by introducing gradient similarity as a loss regularizer to further improve out-of-distribution generalization performance. We also propose combining WA and SAM to solve the problem of few-shot domain adaptation. Our extensive experiments on digits datasets (MNIST, SVHN, USPS, MNIST-M) and other domain adaptation datasets (VLCS, PACS) show that combining WA and SAM leads to improved out-of-distribution generalization performance and significantly increases few-shot domain adaptation accuracy.

Figures

Figures reproduced from arXiv: 2501.08361 by the authors.

Figure 1
Figure 1. , adapted from Model Soups [93], illustrates an interesting phenomenon: there is a strong correlation between the accuracy gain of a weight averaged model and the angles between the models. As shown in the plot, larger angles between two models result in greater accuracy gain for the averaged model. This suggests that increasing model diversity can lead to better performance for the averaged model. However, the auth… view at source ↗
Figure 2
Figure 2. Samples from PACS [52]. From top to bottom are 4 domains: art, cartoon, photo and sketch. From left to right are 7 classes: dog, elephant, giraffe, guitar, horse, house and person. 16 [PITH_FULL_IMAGE:figures/full_fig_p020_2.png] view at source ↗
Figure 3
Figure 3. Samples from VLCS [20]. From top to bottom are 4 domains: Caltech101, LabelMe, SUN09 and VOC2007. From left to right are 5 classes: bird, car, chair, dog and person. Datasets. Following previous works [72, 13], we use DomainBed, which is a fair bench￾mark for evaluating OOD performance. It contains 5 datasets: PACS [52], VLCS [20], Of￾ficeHome [89], TerraIncognita [8], and DomainNet [69]. Due to the time and computa… view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: The effect of the number of models used for weight averaging on PACS. [PITH_FULL_IMAGE:figures/full_fig_p024_4.png]
Figure 5
Figure 5. Figure 5: The effect of the number of models used for weight averaging on VLCS. [PITH_FULL_IMAGE:figures/full_fig_p024_5.png]
Figure 6
Figure 6. Figure 6: Samples from VisDA-C. There are 12 classes in VisDA-C. From left to right and [PITH_FULL_IMAGE:figures/full_fig_p025_6.png]
Figure 7
Figure 7. Figure 7: The effect of the number of adapted samples per class. [PITH_FULL_IMAGE:figures/full_fig_p028_7.png]
Figure 8
Figure 8. Figure 8: t-SNE visualization for ResNet18 (ImageNet pretrained). The top row shows the [PITH_FULL_IMAGE:figures/full_fig_p029_8.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

102 extracted references · 41 canonical work pages

  1. [85]

    Evading the simplicity bias: Training a diverse set of models discovers solutions with superior ood generalization

    Damien Teney, Ehsan Abbasnejad, Simon Lucey, and Anton van den Hengel. Evading the simplicity bias: Training a diverse set of models discovers solutions with superior ood generalization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 16761--16772, 2022

    work page 2022

  2. [1]

    Falk, and Ioannis Mitliagkas

    Isabela Albuquerque, João Monteiro, Tiago H. Falk, and Ioannis Mitliagkas. Adversarial target-invariant representation learning for domain generalization. CoRR , abs/1911.00804, 2019

    arXiv 1911

  3. [2]

    Improving out-of-distribution generalization via multi-task self-supervised pretraining

    Isabela Albuquerque, Nikhil Naik, Junnan Li, Nitish Keskar, and Richard Socher. Improving out-of-distribution generalization via multi-task self-supervised pretraining. arXiv preprint arXiv:2003.13525 , 2020

    arXiv 2003

  4. [3]

    Towards understanding sharpness-aware minimization

    Maksym Andriushchenko and Nicolas Flammarion. Towards understanding sharpness-aware minimization. In International Conference on Machine Learning , pages 639--668. PMLR, 2022

    2022

  5. [4]

    Ensemble of averages: Improving model selection and boosting performance in domain generalization

    Devansh Arpit, Huan Wang, Yingbo Zhou, and Caiming Xiong. Ensemble of averages: Improving model selection and boosting performance in domain generalization. arXiv preprint arXiv:2110.10832 , 2021

    arXiv 2021

  6. [5]

    Objectnet: A large-scale bias-controlled dataset for pushing the limits of object recognition models

    Andrei Barbu, David Mayo, Julian Alverio, William Luo, Christopher Wang, Dan Gutfreund, Josh Tenenbaum, and Boris Katz. Objectnet: A large-scale bias-controlled dataset for pushing the limits of object recognition models. Advances in neural information processing systems , 32, 2019

    2019

  7. [6]

    An empirical comparison of voting classification algorithms: Bagging, boosting, and variants

    Eric Bauer and Ron Kohavi. An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine learning , 36(1):105--139, 1999

    1999

  8. [7]

    A human-centered evaluation of a deep learning system deployed in clinics for the detection of diabetic retinopathy

    Emma Beede, Elizabeth Baylor, Fred Hersch, Anna Iurchenko, Lauren Wilcox, Paisan Ruamviboonsuk, and Laura M Vardoulakis. A human-centered evaluation of a deep learning system deployed in clinics for the detection of diabetic retinopathy. In Proceedings of the 2020 CHI conference on human factors in computing systems , pages 1--12, 2020

    2020

Show all 102 references
  1. [8]

    Recognition in terra incognita

    Sara Beery, Grant Van Horn, and Pietro Perona. Recognition in terra incognita. In Proceedings of the European conference on computer vision (ECCV) , pages 456--473, 2018

    2018

  2. [9]

    On the opportunities and risks of foundation models

    Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al. On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258 , 2021

    2021 arXiv

  3. [10]

    Bagging predictors

    Leo Breiman. Bagging predictors. Machine learning , 24(2):123--140, 1996

    1996

  4. [11]

    Random forests

    Leo Breiman. Random forests. Machine learning , 45(1):5--32, 2001

    2001

  5. [12]

    Language models are few-shot learners

    Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. Advances in neural information processing systems , 33:1877--1901, 2020

    1901

  6. [13]

    Swad: Domain generalization by seeking flat minima

    Junbum Cha, Sanghyuk Chun, Kyungjae Lee, Han-Cheol Cho, Seunghyun Park, Yunsung Lee, and Sungrae Park. Swad: Domain generalization by seeking flat minima. Advances in Neural Information Processing Systems , 34:22405--22418, 2021

    2021

  7. [14]

    Exploiting hierarchical context on a large database of object categories

    Myung Jin Choi, Joseph J Lim, Antonio Torralba, and Alan S Willsky. Exploiting hierarchical context on a large database of object categories. In 2010 IEEE computer society conference on computer vision and pattern recognition , pages 129--136. IEEE, 2010

    2010

  8. [15]

    Imagenet: A large-scale hierarchical image database

    Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition , pages 248--255. Ieee, 2009

    2009

  9. [16]

    Bert: Pre-training of deep bidirectional transformers for language understanding

    Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 , 2018

    2018 arXiv

  10. [17]

    Sharp minima can generalize for deep nets

    Laurent Dinh, Razvan Pascanu, Samy Bengio, and Yoshua Bengio. Sharp minima can generalize for deep nets. In International Conference on Machine Learning , pages 1019--1028. PMLR, 2017

    2017

  11. [18]

    a uble, Manuel W \

    Andrea Dittadi, Frederik Tr \"a uble, Manuel W \"u thrich, Felix Widmaier, Peter Gehler, Ole Winther, Francesco Locatello, Olivier Bachem, Bernhard Sch \"o lkopf, and Stefan Bauer. The role of pretrained representations for the ood generalization of rl agents. arXiv preprint a...

    2021 arXiv

  12. [19]

    The pascal visual object classes (voc) challenge

    Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman. The pascal visual object classes (voc) challenge. International journal of computer vision , 88(2):303--338, 2010

    2010

  13. [20]

    Unbiased metric learning: On the utilization of multiple datasets and web images for softening bias

    Chen Fang, Ye Xu, and Daniel N Rockmore. Unbiased metric learning: On the utilization of multiple datasets and web images for softening bias. In Proceedings of the IEEE International Conference on Computer Vision , pages 1657--1664, 2013

    2013

  14. [21]

    Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories

    Li Fei-Fei, Rob Fergus, and Pietro Perona. Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. In 2004 conference on computer vision and pattern recognition workshop , pages 178--178. IEEE, 2004

    2004

  15. [22]

    Sharpness-aware minimization for efficiently improving generalization

    Pierre Foret, Ariel Kleiner, Hossein Mobahi, and Behnam Neyshabur. Sharpness-aware minimization for efficiently improving generalization. arXiv preprint arXiv:2010.01412 , 2020

    2010 arXiv

  16. [23]

    Linear mode connectivity and the lottery ticket hypothesis

    Jonathan Frankle, Gintare Karolina Dziugaite, Daniel Roy, and Michael Carbin. Linear mode connectivity and the lottery ticket hypothesis. In International Conference on Machine Learning , pages 3259--3269. PMLR, 2020

    2020

  17. [24]

    A decision-theoretic generalization of on-line learning and an application to boosting

    Yoav Freund and Robert E Schapire. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of computer and system sciences , 55(1):119--139, 1997

    1997

  18. [25]

    Unsupervised domain adaptation by backpropagation

    Yaroslav Ganin and Victor Lempitsky. Unsupervised domain adaptation by backpropagation. In International conference on machine learning , pages 1180--1189. PMLR, 2015

    2015

  19. [26]

    Domain-adversarial training of neural networks

    Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, Fran c ois Laviolette, Mario Marchand, and Victor Lempitsky. Domain-adversarial training of neural networks. The journal of machine learning research , 17(1):2096--2030, 2016

    2016

  20. [27]

    Shortcut learning in deep neural networks

    Robert Geirhos, J \"o rn-Henrik Jacobsen, Claudio Michaelis, Richard Zemel, Wieland Brendel, Matthias Bethge, and Felix A Wichmann. Shortcut learning in deep neural networks. Nature Machine Intelligence , 2(11):665--673, 2020

    2020

  21. [28]

    In search of lost domain generalization

    Ishaan Gulrajani and David Lopez-Paz. In search of lost domain generalization. arXiv preprint arXiv:2007.01434 , 2020

    2007 arXiv

  22. [29]

    Stochastic weight averaging revisited

    Hao Guo, Jiyong Jin, and Bin Liu. Stochastic weight averaging revisited. arXiv preprint arXiv:2201.00519 , 2022

    2022 arXiv

  23. [30]

    Stochastic weight averaging in parallel: Large-batch training that generalizes well

    Vipul Gupta, Santiago Akle Serrano, and Dennis DeCoste. Stochastic weight averaging in parallel: Large-batch training that generalizes well. arXiv preprint arXiv:2001.02312 , 2020

    2001 arXiv

  24. [31]

    Momentum contrast for unsupervised visual representation learning

    Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 9729--9738, 2020

    2020

  25. [32]

    Deep residual learning for image recognition

    Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition , pages 770--778, 2016

    2016

  26. [33]

    Benchmarking neural network robustness to common corruptions and perturbations

    Dan Hendrycks and Thomas Dietterich. Benchmarking neural network robustness to common corruptions and perturbations. arXiv preprint arXiv:1903.12261 , 2019

    1903 arXiv

  27. [34]

    What shapes feature representations? exploring datasets, architectures, and training

    Katherine Hermann and Andrew Lampinen. What shapes feature representations? exploring datasets, architectures, and training. Advances in Neural Information Processing Systems , 33:9995--10006, 2020

    2020

  28. [35]

    beta- VAE : Learning basic visual concepts with a constrained variational framework

    Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner. beta- VAE : Learning basic visual concepts with a constrained variational framework. In International Conference on Learning Representations , 2017

    2017

  29. [36]

    Denoising diffusion probabilistic models

    Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems , 33:6840--6851, 2020

    2020

  30. [37]

    Simplifying neural nets by discovering flat minima

    Sepp Hochreiter and J \"u rgen Schmidhuber. Simplifying neural nets by discovering flat minima. Advances in neural information processing systems , 7, 1994

    1994

  31. [38]

    Jonathan J. Hull. A database for handwritten text recognition research. IEEE Transactions on pattern analysis and machine intelligence , 16(5):550--554, 1994

    1994

  32. [39]

    Adversarial examples are not bugs, they are features

    Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, and Aleksander Madry. Adversarial examples are not bugs, they are features. Advances in neural information processing systems , 32, 2019

    2019

  33. [40]

    Averaging weights leads to wider optima and better generalization

    Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov, and Andrew Gordon Wilson. Averaging weights leads to wider optima and better generalization. arXiv preprint arXiv:1803.05407 , 2018

    2018 arXiv

  34. [41]

    Scaling up visual and vision-language representation learning with noisy text supervision

    Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. Scaling up visual and vision-language representation learning with noisy text supervision. In International Conference on Machine Learning , pages 4904--491...

    2021

  35. [42]

    When do flat minima optimizers work? arXiv preprint arXiv:2202.00661 , 2022

    Jean Kaddour, Linqing Liu, Ricardo Silva, and Matt J Kusner. When do flat minima optimizers work? arXiv preprint arXiv:2202.00661 , 2022

    2022 arXiv

  36. [43]

    On large-batch training for deep learning: Generalization gap and sharp minima

    Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, and Ping Tak Peter Tang. On large-batch training for deep learning: Generalization gap and sharp minima. arXiv preprint arXiv:1609.04836 , 2016

    2016 arXiv

  37. [44]

    Generalization in anti-causal learning

    Niki Kilbertus, Giambattista Parascandolo, and Bernhard Sch \"o lkopf. Generalization in anti-causal learning. arXiv preprint arXiv:1812.00524 , 2018

    2018 arXiv

  38. [45]

    Disentangling by factorising

    Hyunjik Kim and Andriy Mnih. Disentangling by factorising. In International Conference on Machine Learning , pages 2649--2658. PMLR, 2018

    2018

  39. [46]

    Last layer re-training is sufficient for robustness to spurious correlations

    Polina Kirichenko, Pavel Izmailov, and Andrew Gordon Wilson. Last layer re-training is sufficient for robustness to spurious correlations. arXiv preprint arXiv:2204.02937 , 2022

    2022 arXiv

  40. [47]

    Big transfer (bit): General visual representation learning

    Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, and Neil Houlsby. Big transfer (bit): General visual representation learning. In European conference on computer vision , pages 491--507. Springer, 2020

    2020

  41. [48]

    Learning multiple layers of features from tiny images, 2009

    Alex Krizhevsky, Geoffrey Hinton, et al. Learning multiple layers of features from tiny images, 2009

    2009

  42. [49]

    Fine-tuning can distort pretrained features and underperform out-of-distribution

    Ananya Kumar, Aditi Raghunathan, Robbie Jones, Tengyu Ma, and Percy Liang. Fine-tuning can distort pretrained features and underperform out-of-distribution. arXiv preprint arXiv:2202.10054 , 2022

    2022 arXiv

  43. [50]

    Asam: Adaptive sharpness-aware minimization for scale-invariant learning of deep neural networks

    Jungmin Kwon, Jeongseop Kim, Hyunseo Park, and In Kwon Choi. Asam: Adaptive sharpness-aware minimization for scale-invariant learning of deep neural networks. In International Conference on Machine Learning , pages 5905--5914. PMLR, 2021

    2021

  44. [51]

    Gradient-based learning applied to document recognition

    Yann LeCun, L \'e on Bottou, Yoshua Bengio, and Patrick Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE , 86(11):2278--2324, 1998

    1998

  45. [52]

    Deeper, broader and artier domain generalization

    Da Li, Yongxin Yang, Yi-Zhe Song, and Timothy M Hospedales. Deeper, broader and artier domain generalization. In Proceedings of the IEEE international conference on computer vision , pages 5542--5550, 2017

    2017

  46. [53]

    Cross-domain adaptive clustering for semi-supervised domain adaptation

    Jichang Li, Guanbin Li, Yemin Shi, and Yizhou Yu. Cross-domain adaptive clustering for semi-supervised domain adaptation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 2505--2514, 2021

    2021

  47. [54]

    Few-shot domain adaptation with polymorphic transformers

    Shaohua Li, Xiuchao Sui, Jie Fu, Huazhu Fu, Xiangde Luo, Yangqin Feng, Xinxing Xu, Yong Liu, Daniel SW Ting, and Rick Siow Mong Goh. Few-shot domain adaptation with polymorphic transformers. In International Conference on Medical Image Computing and Computer-Assisted Intervent...

    2021

  48. [55]

    Domain invariant and class discriminative feature learning for visual domain adaptation

    Shuang Li, Shiji Song, Gao Huang, Zhengming Ding, and Cheng Wu. Domain invariant and class discriminative feature learning for visual domain adaptation. IEEE transactions on image processing , 27(9):4260--4273, 2018

    2018

  49. [56]

    Learning transferable features with deep adaptation networks

    Mingsheng Long, Yue Cao, Jianmin Wang, and Michael Jordan. Learning transferable features with deep adaptation networks. In International conference on machine learning , pages 97--105. PMLR, 2015

    2015

  50. [57]

    Deep transfer learning with joint adaptation networks

    Mingsheng Long, Han Zhu, Jianmin Wang, and Michael I Jordan. Deep transfer learning with joint adaptation networks. In International conference on machine learning , pages 2208--2217. PMLR, 2017

    2017

  51. [58]

    Benchmarking robustness in object detection: Autonomous driving when winter is coming

    Claudio Michaelis, Benjamin Mitzkus, Robert Geirhos, Evgenia Rusak, Oliver Bringmann, Alexander S Ecker, Matthias Bethge, and Wieland Brendel. Benchmarking robustness in object detection: Autonomous driving when winter is coming. arXiv preprint arXiv:1907.07484 , 2019

    1907 arXiv

  52. [59]

    Few-shot adversarial domain adaptation

    Saeid Motiian, Quinn Jones, Seyed Iranmanesh, and Gianfranco Doretto. Few-shot adversarial domain adaptation. Advances in neural information processing systems , 30, 2017

    2017

  53. [60]

    Unified deep supervised domain adaptation and generalization

    Saeid Motiian, Marco Piccirilli, Donald A Adjeroh, and Gianfranco Doretto. Unified deep supervised domain adaptation and generalization. In Proceedings of the IEEE international conference on computer vision , pages 5715--5725, 2017

    2017

  54. [61]

    Domain generalization via invariant feature representation

    Krikamol Muandet, David Balduzzi, and Bernhard Sch \"o lkopf. Domain generalization via invariant feature representation. In International Conference on Machine Learning , pages 10--18. PMLR, 2013

    2013

  55. [62]

    Deep ensembles for low-data transfer learning

    Basil Mustafa, Carlos Riquelme, Joan Puigcerver, Andr \'e Susano Pinto, Daniel Keysers, and Neil Houlsby. Deep ensembles for low-data transfer learning. arXiv preprint arXiv:2010.06866 , 2020

    2010 arXiv

  56. [63]

    Understanding the failure modes of out-of-distribution generalization

    Vaishnavh Nagarajan, Anders Andreassen, and Behnam Neyshabur. Understanding the failure modes of out-of-distribution generalization. arXiv preprint arXiv:2010.15775 , 2020

    2010 arXiv

  57. [64]

    Reading digits in natural images with unsupervised feature learning

    Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Baolin Wu, Andrew Y Ng, et al. Reading digits in natural images with unsupervised feature learning. In NIPS workshop on deep learning and unsupervised feature learning , volume 2011, page 4. Granada, 2011

    2011

  58. [65]

    Exploring generalization in deep learning

    Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, and Nati Srebro. Exploring generalization in deep learning. Advances in neural information processing systems , 30, 2017

    2017

  59. [66]

    What is being transferred in transfer learning? Advances in neural information processing systems , 33:512--523, 2020

    Behnam Neyshabur, Hanie Sedghi, and Chiyuan Zhang. What is being transferred in transfer learning? Advances in neural information processing systems , 33:512--523, 2020

    2020

  60. [67]

    In search of the real inductive bias: On the role of implicit regularization in deep learning

    Behnam Neyshabur, Ryota Tomioka, and Nathan Srebro. In search of the real inductive bias: On the role of implicit regularization in deep learning. arXiv preprint arXiv:1412.6614 , 2014

    2014 arXiv

  61. [68]

    Can you trust your model's uncertainty? evaluating predictive uncertainty under dataset shift

    Yaniv Ovadia, Emily Fertig, Jie Ren, Zachary Nado, David Sculley, Sebastian Nowozin, Joshua Dillon, Balaji Lakshminarayanan, and Jasper Snoek. Can you trust your model's uncertainty? evaluating predictive uncertainty under dataset shift. Advances in neural information processi...

    2019

  62. [69]

    Moment matching for multi-source domain adaptation

    Xingchao Peng, Qinxun Bai, Xide Xia, Zijun Huang, Kate Saenko, and Bo Wang. Moment matching for multi-source domain adaptation. In Proceedings of the IEEE/CVF international conference on computer vision , pages 1406--1415, 2019

    2019

  63. [70]

    Visda: The visual domain adaptation challenge, 2017

    Xingchao Peng, Ben Usman, Neela Kaushik, Judy Hoffman, Dequan Wang, and Kate Saenko. Visda: The visual domain adaptation challenge, 2017

    2017

  64. [71]

    Learning transferable visual models from natural language supervision

    Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning , p...

    2021

  65. [72]

    Diverse weight averaging for out-of-distribution generalization

    Alexandre Rame, Matthieu Kirchmeyer, Thibaud Rahier, Alain Rakotomamonjy, Patrick Gallinari, and Matthieu Cord. Diverse weight averaging for out-of-distribution generalization. arXiv preprint arXiv:2205.09739 , 2022

    2022 arXiv

  66. [73]

    Ensembles of locally independent prediction models

    Andrew Ross, Weiwei Pan, Leo Celi, and Finale Doshi-Velez. Ensembles of locally independent prediction models. In Proceedings of the AAAI Conference on Artificial Intelligence , volume 34, pages 5527--5536, 2020

    2020

  67. [74]

    Optimal representations for covariate shift

    Yangjun Ruan, Yann Dubois, and Chris J Maddison. Optimal representations for covariate shift. arXiv preprint arXiv:2201.00057 , 2021

    2021 arXiv

  68. [75]

    Labelme: a database and web-based tool for image annotation

    Bryan C Russell, Antonio Torralba, Kevin P Murphy, and William T Freeman. Labelme: a database and web-based tool for image annotation. International journal of computer vision , 77(1):157--173, 2008

    2008

  69. [76]

    Out-of-domain detection based on generative adversarial network

    Seonghan Ryu, Sangjun Koo, Hwanjo Yu, and Gary Geunbae Lee. Out-of-domain detection based on generative adversarial network. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing , pages 714--718, Brussels, Belgium, October-November 2018. As...

    2018

  70. [77]

    Ensemble learning: A survey

    Omer Sagi and Lior Rokach. Ensemble learning: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery , 8(4):e1249, 2018

    2018

  71. [78]

    Semi-supervised domain adaptation via minimax entropy

    Kuniaki Saito, Donghyun Kim, Stan Sclaroff, Trevor Darrell, and Kate Saenko. Semi-supervised domain adaptation via minimax entropy. In Proceedings of the IEEE/CVF International Conference on Computer Vision , pages 8050--8058, 2019

    2019

  72. [79]

    Maximum classifier discrepancy for unsupervised domain adaptation

    Kuniaki Saito, Kohei Watanabe, Yoshitaka Ushiku, and Tatsuya Harada. Maximum classifier discrepancy for unsupervised domain adaptation. In Proceedings of the IEEE conference on computer vision and pattern recognition , pages 3723--3732, 2018

    2018

  73. [80]

    Toward causal representation learning

    Bernhard Sch \"o lkopf, Francesco Locatello, Stefan Bauer, Nan Rosemary Ke, Nal Kalchbrenner, Anirudh Goyal, and Yoshua Bengio. Toward causal representation learning. Proceedings of the IEEE , 109(5):612--634, 2021

    2021

  74. [81]

    u gelgen, Frederik Tr \

    Lukas Schott, Julius Von K \"u gelgen, Frederik Tr \"a uble, Peter Gehler, Chris Russell, Matthias Bethge, Bernhard Sch \"o lkopf, Francesco Locatello, and Wieland Brendel. Visual representation learning does not generalize strongly within the same domain. arXiv preprint arXiv...

    2021 arXiv

  75. [82]

    The pitfalls of simplicity bias in neural networks

    Harshay Shah, Kaustav Tamuly, Aditi Raghunathan, Prateek Jain, and Praneeth Netrapalli. The pitfalls of simplicity bias in neural networks. Advances in Neural Information Processing Systems , 33:9573--9585, 2020

    2020

  76. [83]

    Return of frustratingly easy domain adaptation

    Baochen Sun, Jiashi Feng, and Kate Saenko. Return of frustratingly easy domain adaptation. corr. arXiv preprint arXiv:1511.05547 , 2015

    2015 arXiv

  77. [84]

    Intriguing properties of neural networks

    Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 , 2013

    2013 arXiv

  78. [86]

    Few-shot domain adaptation by causal mechanism transfer

    Takeshi Teshima, Issei Sato, and Masashi Sugiyama. Few-shot domain adaptation by causal mechanism transfer. In International Conference on Machine Learning , pages 9458--9469. PMLR, 2020

    2020

  79. [87]

    Adversarial discriminative domain adaptation

    Eric Tzeng, Judy Hoffman, Kate Saenko, and Trevor Darrell. Adversarial discriminative domain adaptation. In Proceedings of the IEEE conference on computer vision and pattern recognition , pages 7167--7176, 2017

    2017

  80. [88]

    Visualizing data using t-sne

    Laurens Van der Maaten and Geoffrey Hinton. Visualizing data using t-sne. Journal of machine learning research , 9(11), 2008

    2008

  81. [89]

    Deep hashing network for unsupervised domain adaptation

    Hemanth Venkateswara, Jose Eusebio, Shayok Chakraborty, and Sethuraman Panchanathan. Deep hashing network for unsupervised domain adaptation. In Proceedings of the IEEE conference on computer vision and pattern recognition , pages 5018--5027, 2017

    2017

  82. [90]

    Multimodal self-supervised learning of general audio representations

    Luyu Wang, Pauline Luc, Adria Recasens, Jean-Baptiste Alayrac, and Aaron van den Oord. Multimodal self-supervised learning of general audio representations. arXiv preprint arXiv:2104.12807 , 2021

    2021 arXiv

  83. [91]

    Out of distribution robustness with pre-trained bayesian neural networks

    Xi Wang and Laurence Aitchison. Out of distribution robustness with pre-trained bayesian neural networks. arXiv preprint arXiv:2206.12361 , 2022

    2022 arXiv

  84. [92]

    Assaying out-of-distribution generalization in transfer learning

    Florian Wenzel, Andrea Dittadi, Peter Vincent Gehler, Carl-Johann Simon-Gabriel, Max Horn, Dominik Zietlow, David Kernert, Chris Russell, Thomas Brox, Bernt Schiele, et al. Assaying out-of-distribution generalization in transfer learning. arXiv preprint arXiv:2207.09239 , 2022

    2022 arXiv

  85. [93]

    Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time

    Mitchell Wortsman, Gabriel Ilharco, Samir Ya Gadre, Rebecca Roelofs, Raphael Gontijo-Lopes, Ari S Morcos, Hongseok Namkoong, Ali Farhadi, Yair Carmon, Simon Kornblith, et al. Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing infe...

    2022

  86. [94]

    Extrapolation and spectral bias of neural nets with hadamard product: a polynomial net study

    Yongtao Wu, Zhenyu Zhu, Fanghui Liu, Grigorios G Chrysos, and Volkan Cevher. Extrapolation and spectral bias of neural nets with hadamard product: a polynomial net study. arXiv preprint arXiv:2209.07736 , 2022

    2022 arXiv

  87. [95]

    How neural networks extrapolate: From feedforward to graph neural networks

    Keyulu Xu, Mozhi Zhang, Jingling Li, Simon S Du, Ken-ichi Kawarabayashi, and Stefanie Jegelka. How neural networks extrapolate: From feedforward to graph neural networks. arXiv preprint arXiv:2009.11848 , 2020

    2009 arXiv

  88. [96]

    d-sne: Domain adaptation using stochastic neighborhood embedding

    Xiang Xu, Xiong Zhou, Ragav Venkatesan, Gurumurthy Swaminathan, and Orchid Majumder. d-sne: Domain adaptation using stochastic neighborhood embedding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 2497--2506, 2019

    2019

  89. [97]

    Billion-scale semi-supervised learning for image classification

    I Zeki Yalniz, Herv \'e J \'e gou, Kan Chen, Manohar Paluri, and Dhruv Mahajan. Billion-scale semi-supervised learning for image classification. arXiv preprint arXiv:1905.00546 , 2019

    1905 arXiv

  90. [98]

    Improved ood generalization via adversarial training and pretraing

    Mingyang Yi, Lu Hou, Jiacheng Sun, Lifeng Shang, Xin Jiang, Qun Liu, and Zhiming Ma. Improved ood generalization via adversarial training and pretraing. In International Conference on Machine Learning , pages 11987--11997. PMLR, 2021

    2021

  91. [99]

    Coca: Contrastive captioners are image-text foundation models

    Jiahui Yu, Zirui Wang, Vijay Vasudevan, Legg Yeung, Mojtaba Seyedhosseini, and Yonghui Wu. Coca: Contrastive captioners are image-text foundation models. arXiv preprint arXiv:2205.01917 , 2022

    2022 arXiv

  92. [100]

    Understanding deep learning (still) requires rethinking generalization

    Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM , 64(3):107--115, 2021

    2021

  93. [101]

    mixup: Beyond empirical risk minimization

    Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz. mixup: Beyond empirical risk minimization. arXiv preprint arXiv:1710.09412 , 2017

    2017 arXiv

  94. [102]

    Few-shot adaptation of pre-trained networks for domain shift

    Wenyu Zhang, Li Shen, Wanyue Zhang, and Chuan-Sheng Foo. Few-shot adaptation of pre-trained networks for domain shift. arXiv preprint arXiv:2205.15234 , 2022

    2022 arXiv

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.