Towards domain-invariant Self-Supervised Learning with Batch Styles Standardization
Authors: Marin Scalbert
Maria Vakalopoulou
Florent Couzinie-Devy
Venue: ICLR 2024 poster (2024)
Keywords: Self-Supervised Learning
Unsupervised Domain Generalization
Distribution Shifts
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
TL;DR: Batch Styles Standardization, a method to standardizes the styles of images in a batch, designed to be combined with existing SSL approaches to reduce spurious correlations and promote domain-invariance within SSL representations.

Abstract: In Self-Supervised Learning (SSL), models are typically pretrained, fine-tuned, and evaluated on the same domains. However, they tend to perform poorly when evaluated on unseen domains, a challenge that Unsupervised Domain Generalization (UDG) seeks to address. Current UDG methods rely on domain labels, which are often challenging to collect, and domain-specific architectures that lack scalability when confronted with numerous domains, making the current methodology impractical and rigid. Inspired by contrastive-based UDG methods that mitigate spurious correlations by restricting comparisons to examples from the same domain, we hypothesize that eliminating style variability within a batch could provide a more convenient and flexible way to reduce spurious correlations without requiring domain labels. To verify this hypothesis, we introduce Batch Styles Standardization (BSS), a relatively simple yet powerful Fourier-based method to standardize the style of images in a batch specifically designed for integration with SSL methods to tackle UDG. Combining BSS with existing SSL methods offers serious advantages over prior UDG methods: (1) It eliminates the need for domain labels or domain-specific network components to enhance domain-invariance in SSL representations, and (2) offers flexibility as BSS can be seamlessly integrated with diverse contrastive-based but also non-contrastive-based SSL methods. Experiments on several UDG datasets demonstrate that it significantly improves downstream task performances on unseen domains, often outperforming or rivaling UDG methods. Finally, this work clarifies the underlying mechanisms contributing to BSS's effectiveness in improving domain-invariance in SSL representations and performances on unseen domains. Implementations of the extended SSL methods and BSS are provided at this [url](https://gitlab.com/vitadx/articles/towards-domain-invariant-ssl-through-bss).

Published as a conference paper at ICLR 2024
TOWARDS DOMAIN-INVARIANT SELF-SUPERVISED LEARNING WITH BATCH STYLES STANDARDIZATION
Marin Scalbert & Maria Vakalopoulou
Florent Couzinié-Devy
MICS, CentraleSupélec, Université Paris-Saclay Gif-sur-Yvette Yeette France {name. surname centraalsuueeee Fr
VitaDX
Paris, France f. f.cour.nlee-ddyyyetttad..co
ABSTRACT
In Self-Supervised Learning (SSL), models are typically pretrained, fine-tuned, and evaluated on the same domains. However, they tend to perform poorly when evaluated unseen domains, a challenge that Unsupervised Domain Generaliza- tion (UDG) seeks to address. Current UDG methods rely on domain labels, which are often challenging to collect, and domain-spectie architectures that lack scala- bility when confronted with numerous domains, making the current methodology impractical and rigid. Inspired by contrastive-based UDG methods that mitigate spurious correlations by restricting comparisons to examples from the same do- main, hypothesize that eliminating style variability within batch could pro- vide more convenient and flexible way to reduce spurious correlations without requiring domain labels. To verify this hypothesis, we introduce Batch Styles Standardization (BSS), relatively simple yet powerful Fourier-based method to standardize the style of images in batch specifically designed for integration with SSL methods to tackle UDG. Combining BSS with existing SSL methods offers serious advantages over prior UDG methods: (1) It eliminates the need for domain labels or domain-specific network components to enhance domain-invariance in SSL representations, and (2) offers flexibility as BSS can be scamlessly inte- SSL meth-
grated with diverse contrastive-based but also non-comtrastive-baet ods. Experiments on several UDG datasets demonstrate that significantly im- proves downstream task performances unseen domains, often outperforming or rivaling UDG methods. Finally, this work clarifies the underlying mechanisms contributing BSS's effectiveness in improving sentations and performances on unseen domains. Implementations of the extended SSL methods and BSS are provided at this url.
in SSL repre-
INTRODUCTION
Motivations. In recent years, Self-Supervised Learning (SSL), has seen significant growth and suc- cess (Chen et al., 2020a; Grill et al., 2020; Caron et al., 2020; 2021; Assran et al., 2022; Bardes al., 2021: He al.. 2022). However, SSL generally assumes that pretraining, fine-tuning and testing data come from the same domains, an assumption which does not hold true in practice and thereby limits its real-life applications. The distribution shifts between pretraining/fie-tune -unnin do- mains (sources domains) and testing domains (targets domains) usually lead to poor generalization on testing domains.
Unsupervised Domain Generalization (UDG) (Zhang et al., 2022), aims to tackle this issue by evalu- ating how well fine-tuned SSL. models generalize to unseen target domains. UDG, models are first pretrained on unlabeled data, fine-tuned on labeled data and finally evaluated on data from unseen domains. This work focuses on the all-correlated UDG setting which is the most standard and stud- ied one (Zhang et al., 2022; Harary et al., 2022; Yang et al., 2022b). In this setting, unlabeled and labeled data come from the same source domains, testing data come from unseen target domains, while all cover the same classes.
Current UDG methods suffer from the same drawbacks: (1) They require domain labels to rein- force nomain-ivvariance in SSL representations, while in practice these labels may be challenging

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Published as a conference paper at ICLR 2024
to obtain or even unavailable and (2) they all rely on domain-speciffe architectures, such domain- specific negative queues or domain-specific decoders, that lack scalability when confronted to nu- merous domains. These limitations highlight the need for more practical and flexible UDG methods.
Taking inspiration from contrastive-based UDG methods that reduce spurious correlations by re- straining comparisons to examples from the same domain, believe that removing style variability within a batch through style standardization may provide a more practical and flexible way to miti- gate spurious correlations and achieve domain-invariant SSL representations without requiring any domain labels.
Contributions. To investigate the effectiveness of style standardization in mitigating spurious cor- relations within SSL representations with the aim of proposing more convenient and flexible UDG approaches, we introduce Batch Styles Standardization (BSS). BSS is simple yet powerful Fourier- based method for standardizing image styles within a batch purposefully designed for integration with existing SSL methods to reinforce -nnaainnivvriince.. Style standardization is performed by transferring the style of a randomly selected image to all images in the batch.
Integrated with existing SSL methods, it confers significant advantages over prior UDG works: (1) reinforces domain-invariance without requiring any domain labels or domain-specinf architecture, and (2) offers simplicity and flexibility, integrating easily with contrastive-base (SimCLR (Chen et al., 2020a), SWaV (Caron et al., 2020)) but also can--ontrastiee-aaeee. SSL methods (MSN (Ass- ran et al., 2022)).
Experiments conducted on UDG datasets indicate that BSS combined with the different SSL meth- ods yields significant performance gains on unseen domains while outperforming or competing with established UDG methods. Finally, extensive experiments have been conducted to clarify the under- lying mechanisms driving BSS's effectiveness in enhancing domain-invariance in SSL representa- tions and performances on unseen domains.
2 RELATED WORKS
Domain Generalization. DG aims to learn a model from multiple source domains with distinct distributions to generalize well unseen target domains. Former DG methods have focused on aligning source features distributions using large panel of techniques (Ganin et al., 2016; Kang al., 2019; Li al., 2018a;b; Peng et al., 2019; Scalbert al., 2021: Zhao et al., 2020). Recently, the trend has shifted towards improving cross-domain generalization by refining data augmentation strategies. These strategies can be applied at either the image level (Scalbert et al., 2022; Xu al., 2021; Yang & Soatto, 2020; Zhou et al., 2020a;b) or the feature representation level (Kang et al., 2022: Li et al., 2021: Zhou et al., 2021), and can be non-parametric (Xu et al., 2021; Zhou et al., 2021), trained adversarially during the DG task (Hoffman et al., 2018; Kang et al., 2022; Zhou al., 2020a;b), or pretrained beforehand on source domains (Scalbert et al., 2022). Among these methods, Fourier-based Augmentations (FA) (Xu al., 2021; Yang & Soatto, 2020) stand out as simple and promising approach to instill domain-invariance into the representations and, thereby, enhance generalization. In this work, the proposed BSS extends FA's style transfer ability to standardize the style of images within batch so as to strengthen
in SSL methods.
Self-Supervised Learning. SSL has gained a lot of attention for its ability to efficiently pretrain models on abundant unlabeled data and subsequently fine-tune them for downstream tasks with limited labeled data. Contrastive and non-contrastive-based methods have emerged as successful approaches. The former focuses on making representations of similar examples (positives) closer while pushing apart representations of dissimilar examples (negatives). Similar examples are usually built by generating several augmented views of the same image. These methods operate either at the instance-level (Chen et al., 2020a;c; Hu et al., 2021) or cluster-level (Caron et al., 2018; 2020). large number of negatives, non-cootraative--asee based methods have attempted
Given their reliance to climinate the use of negative examples but require additional tricks to avoid collapse (Grill et al., 2020; Chen & He, 2021; Caron et al., 2021). In this work, harnessing FA and the proposed BSS, (SimCLR, SWaV) and non-contrastive-bee based (MSN) methods to
we extend both contrastive-bar strengthen -amain-invarianee and address UDG.
Unsupervised Domain Generalization. Contrastive-based UDG methods (DARLING (Zhang et al., 2022), BrAD (Harary et al., 2022)) improve domain-invariance by ensuring that positive
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Published as a conference paper at ICLR 2024
and negative examples share the same domain. This constraint mitigates spurious correlations within SSL representations when repelling negative examples from positive ones. To respect this constraint, DARLING exploits domain adversarial negative queues while BrAD maintains domain-specific negative queues containing past representations of momentum encoder. Addi- tionally, BrAD learns image-to-image mappings from the different domains shared space and compares representations of raw and projected images, As an alternative, DiMAE (Yang et al., 2022b) and CycleMAE (Yang et al., 2022a) rely on Masked Auto-Encoder (He et al., 2022) (MAE) with domain-specifee decoders to solve cross-domas reconstruction task. However, these methods rely on domain labels and complex domain-spp -speciic architectures, limiting scalability and adaptabil- ity. Inspired by UDG contrastive methods, we propose removing style variability within batch and without domain labels to reduce spurious correlations in SSL methods resulting in simpler and more flexible UDG approaches.
METHOD
3.1 PROBLEM FORMULATION
In the all-correlated UDG setting, an unlabeled dataset, a labeled dataset and test dataset are provided. Unlabeled and labeled training data drawn from the source domains D3 while testing data are drawn from unseen target domains Dr. All data share the same class labels space D. The goal of UDG is to pretrain model on the unlabeled data, fine-tune the labeled data and achieve good generalization on the test dataset.
3.2
BATCH STYLES STANDARDIZATION
3.2.1
PRELIMINARIES ON FOURIER-BASED AUGMENTATIONS
Fourier-based Augmentations (Xu et al., 2021; Yang Soatto, 2020) are motivated by a property of the Fourier transform: phase components tend to retain semantic information while amplitude components the style information such as intensity and textures. Therefore, to make the network prioritize semantics over style, FA randomly alters the amplitudes of images during training.
H its Fourier transform F(X) along with the corre- sponding amplitude A(X) and phase P(X) computed as follows:
More formally, given an image
W
W Σ
-m2m
F(X)(u,)) ΣV
X
Xnwwe
W
= Au,) (ip-)
(2)
Im (F(X))
where A(X) V /Re (F(X))² + Im (F(X))² and P(X) arctan
Re (F(X))
The amplitude A(X) then altered by substituting its low-frequency components with those of a randomly selected image A(X') resulting in the altered amplitude Á(X):
Á(X)(u, v) ( (A(X)),,,) if surr* r H and S r W, (u, if H and r W,
(4)
The strength of the augmentation controlled by the hyperparameter r ~ Urrmin: Tmea) represent- ing the ratio between the substituted amplitude area and the entire amplitude area, where Tmin and Tmaz stand for the minimum and maximum possible ratios. Finally, augmented image X with the same content as the original image X and style as the randomly chosen image X' can be built by applying the inverse Fourier transform FF1 onto the altered amplitude Á(X) and unmodified phase P(X):
= F-1 ((X))=<P(XX)
(5)
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3.2.2 EXTEND FOURIER- BASED AUGMENTATIONS FOR BATCH STYLES STANDARDIZATION
Drawing inspiration from contrastive-base UDG methods, we believe that removing style variabil- ity within a batch might reduce spurious correlations in SSL methods without requiring domain labels resulting simpler and more flexible UDG approaches. Since FA possess a style transfer- like ability. they can be extended to perform styles standardzaation/armmonzzzz be accomplished by transferring the style of a randomly chosen image within the batch to all other images in that batch. Hence, the proposed method is referred to Batch Styles Standardization.
Concretely, it can
The process of applying BSS illustrated on Figure la. Specifically, given batch images and their corresponding Fourier transforms, manipulate the different amplitudes by substituting their low-frequency components with those of single randomly chosen image. Finally, after applying the inverse Fourier transform to the different modified Fourier transforms, the style of the randomly chosen image is transferred to all images, effectively handdardziing/hrroonziigg the style. A pseudo- code along with PyTorch implementation of BSS are provided in Appendix A.
To highlight batch-level differences between standard FA and the proposed BSS, we display in Figure lb and Figure 1c, N x V grid of augmented images generated by applying FA or BSS V times on a batch of N images. For a specific view index (column index), it is clear that augmented images produced by FA exhibit different styles whereas in the case of BSS, a unique style prevails. is important to notice that standardized images can undergo independent geometric augmentations, color augmentations must be batch-wise to preserve the unique style.
Phase P Amplitude A
Number angmented
V ; Number of augmented views
Transfer
N Batch sime
N Batch sloee
F
Transfer
(a)
(b)
(c)
Figure 1: (a) BSS: Fourier Transform F is applied all batch images then low-frequency com- ponents of the amplitudes A (determined by the areas ratio r) are replaced by those randomly chosen image (the first one in this case). Finally, inverse Fourier transform FF1 is applied to the altered Fourier transforms to build images with standardized styles. (b) Augmented images with standard independent FA. (c) Augmented images with BSS.
As opposed UDG methods exploiting domain labels to restrict comparisons to examples from the same domain, standardizing the style of examples using random style simulates as if they were drawn from the same "pseudo-domain", thereby eliminating the need for domain labels. Moreover, BSS's seamless integration into existing SSL methods removes the need for UDG domain specific components, such as domain-specific negative queues (DARLING, BRaD) or domain -spccffc de- coders (DiMAE). Collectively, these characteristics position BSS simpler and more versatile solution to enhance domain-invariane within SSL methods and address UDG.
3.3 How TO INTEGRATE BATCH STYLES STANDARDIZATION INTO SSL METHIODS?
Both contrastive and non-contrastive methods aim to distribute batch examples over the embedding space. This distribution can be driven by explicit contrastive loss (SimCLR) or methods prevent- ing representation collapse like the Sinkhom-- Knopp algorithm (SWaV, MSN), centering (DINO), or variance regularization (VicReg). However, when dealing with diverse domains/styles within batch, distributing examples may unintentionally group them by domains/styles, resulting in spuri-
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correlations. To mitigate this, we propose applying BSS to examples undergoing this distribu- tion. Removing style information through standardization should encourage the distribution to focus more example semantics. In the following sections, we extend three existing SSL methods (Sim- CLR, SWaV and MSN) and detail where and how BSS should be integrated. For further technical details about the regular SSL methods, readers may refer to Appendix B the original papers.
SIMCLR aims to bring representations of several views of the same image closer (positives) while repelling all other images representations (negatives). In standard SimCLR, each image in batch of N images is augmented V times resulting in V positive examples. However, this can result in positive examples with domains/styles that differ from those of the negatives (see Figure lb), risking unintentional exploitation of this style/domain discrepancy to solve the contrastive task and causing spurious correlations. To address this, we suggest independently applying BSS V times to the initial batch, ensuring that positive and negative examples share the same styles (see Figure Ic).
SWAV computes representations of several views of the same image and clusters them using an online algorithm. Given that representations should capture similar information, SWaV enforces consistency between representations and cluster assignments produced from different views. To ob- tain these cluster assignments, the Sinkhorn-Klopp (SK) algorithm (Cuturi, 2013) is performed on the representations. Concretely, SK solves an optimal transport problem whose constraints are to assign representations to the most similar centroids/prototyped while keeping uniform assignment distribution over pentroids/prototypss. However, if several domains/styles are present within views subject SK, there is risk of assigning and grouping the corresponding representations using do- main/style information resulting in spurious correlations. To address this, we propose to standardize the style of views subject SK using BSS. In practice, SWaV employs a multi-crop strategy, gen- erating 2 global views (large crops) and V local views (small crops) for each image. In this setting, cluster assignments are computed only from the global views while representations are derived from all views. Therefore. we suggest applying BSS only on the global views and augmenting the lo- cal views using FA. As this results in two batches of global views, each with its own style, SK performed on each batch separately.
MSN aims to match the representations of masked views of the same image with that of an un- masked view. To derive view's representation, MSN computes similarities between its embedding and a set of learnable cluster centroids/protooypes and subsequently transforms them into a prob- ability distribution. Since direct matching between masked and unmasked views' representations can lead to representation collapse, MSN simultaneously optimizes a cross-entrope term along with an entropy regularization term on the mean representation of the masked views. This regularization Additionally, MSN employs
term encourages the model to use the entire set of centroids/prototyyess SK on the representations of the unmasked views to avoid tuning the hyperparameter weighting the entropy regularization term. Similarly to SWaV, if several domains/styles are present within un- masked views, assigning the corresponding representations using SK may group examples using domain/style information. To address this, we propose to standardize the style of unmasked views using BSS while we recommend augmenting masked views using FA.
RESULTS
4.1 DATASETS
To evaluate the extended SSL methods, experiments were conducted on 3 datasets commonly used for benchmarking DG / UDG methods, namely PACS, DomainNet and Camelyon17 WILDS.
PACS (Li et al., 2017) contains 4 domains (photo, art painting, cartoon, sketch) and 7 classes. DomainNet (Peng et al., 2019) contains different domains (clipart, infograph, quickdraw, paint- ing, real and sketch) and covers 345 classes. Following prior UDG works (Harary et al., 2022; Yang et al., 2022b; Zhang et al., 2022), a subset of DomainNet including 20 classes out of the 345 available classes is considered. Camelyon17 WILDS (Koh et al., 2021) includes images covering 2 classes (tumor, no tumor) from 3 domains (hospitals). It is split into train, val, and test. subsets comprising respectively 3, 1, and distinct domains.
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Table I: UDG performances on PACS. Best methods are highlighted in bold.
Proction:
Target
Tareet doenain
sng:
McCa V2
22.97
21.87
28.11
SuaCLR
30.94
25.30
25.03
35.92
35.31
36.34
BYOL.
11.20
16.21
10.01
12.99
17.79
21.87
AdCo
26
17:11
21.96
23.33
12.59
$7.65
18.21
28.52
30.35
51.16
MAF
50.22
23.54
20.78
24.53
24.89
12.69
34.61
22.35
10.44
18.77
DARLING
27.78
19.80
22.51
20.54
26
44.6
39.25
36.1
36.53
59.20
DIMAE
45.85
51.73
25,83
32,50
34.73
50,00
41.25
34.40
35.00
40,91
BrAA"
61.81
36,37
41.35
50.88
52,05
CydeeMEE
52.63
34,85
39.82
42.15
45.43
SmnCLR
41.55
50.16
SunCLR
43.31
46.91
0.99)
$6.37
1.099
55.69
SWaY IVS
2615
27.37
33227
41764
0035
48.1
$4.00
SWaY MSS
39794
02.59
38.50
5.39
50
8.04)
45.00
9.055
5381
52.1
50,00
5.00
Praction: 1098
Labd Fruniow
Mathods
Targzt ckonssn
Tarya ieemsin
platt
curloss
-01
12:00
V2
24.07
32.64
28.58
25.04
33.73
10.02
AdCC
30.31
22.96
50.19
MAE
35.89
25.59
32.39
31,79
25.24
32.25
34.45
32,20
DARLING
53357
39.91
30.17
12.46
56.99
51,20
DIMAE
27.87
39.25
58.65
59.44
BrAD"
72.17
53.66
55.51
CycleMAR"
SanCIR wr
02.07
40.00
35.70
35.32
31043
78.36
30211
55:166
63:30
56.03
SaaCLR w/ BSS
11.291100002)
51.57
(1 4000)
59.43
(1 4.040
66.09
(1 7.77)
60.04
(1 5.613
1:14)
62.75
9.80y 65.67 0.53)
73 9.433
70.25 0.00
SWGY w7
4X27
44.03
20127
30010
47.80
77.30
37.39
06.22
00008
56.15
SWaY wi USS
17.82
11.60)
43.91
(1 1.280 53.68
(1 9.88)
58.87
(1 6.00)
53.27
5.40)
78.62
1.130
59.55
c 2.16) 06 05.40
1,08)
67.80
cT 1.739
67.87 1.000
Uses Imagenet transfer learning.
4.2 EXPERIMENTAL SETUP
Following the standard UDG evaluation protocol (Zhang et al., 2022), models were pretrained on source data in unsupervised way, fine-tuned on fraction of the source data and finally evaluated on the target data. For the pretraining step, all our models were trained using FA or BSS without Imagenet (Deng et al., 2009) transfer learning, except on DomainNet to allow fair comparisons with prior UDG works. In Appendix D.I, the choice of using transfer learning within a DG/UDG context is further discussed while additional experiments unfairly from ImageNet transfer learning. For the fine-tuning step, following BrAD, on PACS and DomainNet, all the models were fine-tuned via linear probing except when considering the entire PACS dataset where full fine-tuning was performed like DARLING and DIMAE. On Camelyon17 WILDS, linear probing was performed for each fraction of labeled data. Pretraining and fine-tuning implementation details are provided in Appendix C.
PACS reveal that SSL methods tend benefit
4.3 EXPERIMENTAL RESULTS
In the following experiments, the proposed extended SSL methods are compared to regular SSL methods (MoCo V2 (Chen et al., 2020c), SimCLR V2 (Chen et al., 2020b), BYOL (Grill et al., 2020), AdCo (Hu et al., 2021), MAE (He al., 2022)) and UDG methods (DARLING, DIMAE, BRaD, CycleMAE). For Camelyon17 WILDS, the extended SSL methods are compared to reim- plemented UDG methods (DARLING, DiMAE) but also the Semi-Supervised Learning method FixMatch (Sohn et al., 2020) and SSL method SWaV trained with additional data from the target.
PACS. For each combination of (sources, target) domains, each fraction of labeled data and each of our SSL models, averaged accuracy over 3 independent runs are reported on Table 1. Compared to FA, integrating BSS to SimCLR or SWaV, significantly improves the overall accuracy (avg.): SimCLR + (+2.96%, +4.55%, +3.61 %. +3.6%); SWaV - (+5.3% +5.9%, +5.46%, +1.2%% for the fractions of labeled data 1%, 5%, 10% and 100%, respectively. Extended SSL methods with BSS outperform most of the time other methods, except for the target domain photo (BrAD, DiMAE, CycleMAE) or in the 10% (DiMAE) and 100% (DiMAE, CycleMAE) labeled data settings. For the target domain photo, one possible explanation is that other methods benefit from transfer learning on ImageNet while for the 10% and 100% labeled data settings, DiMAE and CycleMAE consider different experimental settings (ViT-base architecture, full fine-tuning 10%.)

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DomainNet. Following prior UDG methods, painting, real and sketch were selected source do- mains and others as target domains. The reversed domains combination was also considered. For these two combinations, for each fraction of labeled data (1%, 5% and 10%) and each of our SSL models, we report on Table 2, the accuracy on each target domain, the per-domain averaged accuracy and the overall accuracy. The presented results are averaged over 3 independent runs. When inte-
Table 2: UDG performances on DomainNet subset. Best methods are highlighted in bold.
Sourves Tirget
paininng U real skeich
chparr injogruphi U quickrraw
Inforrphi
guickdraw
peintiing Praction
skatch
Overall
Arg.
ERM
6.51
2.96
3.00
6.68
6.97
7.25
5.88
3.90
BYOL
6.21
3,48
4.27
5,00
8.47
4.42
5.61
5.31
18.85
12.12
AdCo
16.16
12.26
5.65
11.13
16.53
17.19
12.47
13.15
SimCLR V2
23,51
15.42
5.29
20.25
17.84
18.85
15.46
16.86
DARLING
18.53
10.62
12.65
14.45
21.68
21.30
16.56
16.51
26.52
BrAD
47,26
16.89
23.74
20.03
25.08
31.67
25.85
27.45
CycleMAE
37,54
18.01
17.13
22.85
30.38
22.31
21.08
24.71
SimCLR w/ FA
00.83
18.42
26.31
24.29
29.73
40.29
30.82
33.31
61.94
(1
(1
41.49 1.21
1.45)
34.82 1.63)
SWaV w/ FA
59.27
20,95
18.91
30,99
35.73
15.28
31,87
35.19
SWaV w/ BSS
60.40
20.12
0.83)
23.09
4.355
34.64 (1 3.60)
38.45
2172.7
46,90 1,022
34.32
2.45)
37,27
2.08)
Label Fraction 33%
ERM
10.21
7,088
5.34
7.45
6.08
5,00
0.50
6.86
9.78
3,97
7.83
7.53
McCa V2
28.13
13.79
9.67
20.80
24.91
21 21.44
18.99
19.79
AdCo
30.77
18.65
7.75
19.97
24.31
24.19
19.42
20.94
SimCIA
34.01
17.17
24.34
DARLING
DIMAE
42.31
18.87
15.00
27.02
39.92
26.50
27.85
28.27
ReAD
64.01
25.02
29.64
29. 29.32
$4.95
44.09
35,37
37,84
CycleMAE
55,14
20.87
19.62
27.64
40.24
28.71
30,80
32,04
SimCLR FA
69:04
20.31
29.76
41.95
$1.05
38.60
SimCLR w/ BSS
71.21 (1 2.177)
20.93 0.82))
32:42 2.88)
36.68 (1 0.24)
41.49
0.465
52.75 <T 1.7)
39.73
1.13)
42.58
(1 1.16)
SWaV w/ FA
68.84
24.05
26.06
43.97
49.11
59.16
41.68
45,20
1,72)
24.35
(1
2,77)
(1 2.2)
2.33
0.53)
Label
Fraction 10%
15.10
9.19
5.12
8:94
BYOL
14.55
8,71
3.95
9.50
10.38
1.45
8.69
8.92
MoCo V2
32,46
18.54
8:05
25.35
29.91
23.71
21.87
23,00
AdCo
32.25
17.96
11.56
23.35
SinCLR V2
37.11
19,87
12.33
24.01
30.17
31.58
24.28
25.84
DARLING
35.15
20.88
13.69
25.90
33.29
30.77
26.09
26.05
DIMAR (fill line-tune)
70.78
38.06
27.39
50.73
64.89
55.41
49.49
$1,21
68.27
CycleMARrail lino-tuns)
74.87
3W.42
28.32
52.81
67.13
56.37
50.78
52,98
SimCLR w/ FA
20.12
20.30
31.21
39.16
41.15
$2.87
10.31
43.05
SimCLR w/ BSS
71.95 1.83)
21.27 0.77)
33.47 2:244)
39.49 (1 0.33)
44.67 (1 0.22)
55.42
41.57
1.26)
44,38 1.33)
SWaV w/ BSS
71.99 2.18)
24.34
0.05)
29.82
0.853
48.28 (1 2.56)
$2.37
(1 1.58)
60:35
0.25)
44.59
1.18) 47,89 1.133
grating BSS, almost all target domain accuracies increase while per-domain accuracy al- ways improves: SimCLR - (+1.51%, +1.16%, +1.33%) SWaV + +1. +1.11%) for the labeled data fractions 1%, 5% and 10%, respectively.
Camelyon17 WILDS. Averaged accuracy over 10 independent runs, for different fractions of la- beled data, on the test split are reported on Table To ensure fair comparisons, the reimplemented methods DARLING and DiMAE used identical pretraining and fine-tuning hyperparameters as ex- tended SSL methods. For each extended SSL method, BSS leads substantial performance gains
Table 3: UDG performances on Camelyon17 17 WILDS, Best methods are highlighted in bold.
Label Fraction
Method
1%
5%
10%
100%
DARLING
70.44
72,00
72.43
72.36
DIMAE"
89.81
89.17
89.77
90.40
FixMaich
X
X
X
71.00
SWav1
x
X
91.40
SumCLR FA
90.82
93.00
97.94
93.44
SinCLR BSS
94.75
1.88)
<1 1.56)
SWaV w/FA
91.05
91.44
91.96
92.26
SWaV w/ BSS
93.42
2.39)
93.99
2.05)
94.07
2.111
94.08
1.888)
MSN w/
87.07
87.33
87,55
85.82
MSN w/ BSS
90.93
(1 3.86)
91.82
4.499
91.83
4.28)
91.98
(1 3.765
OOu implementation (no available public code).
1 From WILDS challenge (Koh et al., 2021). Uses unlabeled data from the target domain.
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Published as a conference paper at ICLR 2024
ranging from +1.4%% to +4.49% resulting in state-of-the-ee performances. Extended SSL methods with BSS even surpass methods trained with additional unlabeled target data.
ABLATION STUDIES AND ADDITIONAL EXPERIMENTS
5.1 AUGMENTATION STRATEGY ABLATION STUDY
To evaluate the benefits of each component in augmentation strategy (BSS additional aug- mentations) on performances, we pretrained a model on Camelyon17 WILDS using SimCLR and different combinations of components. For each combination and each fraction of labeled data, eraged accuracy over 10 independent runs are reported in Table 4. Sample-wise color jittering and
Table 4: SimCLR's performances on Camelyon17 WILDS varying our augmentation strategy's components. Best methods are highlighted in bold.
Label Fraction
Color-jitter
FA
BSS
1%
5%
10%
100%
Sample-wise
63.54
63.58
63.50
65.06
Batch-wise
66.81
68.19
68.45
68.53
Sample-wise
X
90.82
93.00
92.94
93.44
Batch-wise
X
92.27
94.75
94.82
95.00
no FA or BSS, resulting in the regular SimCLR, leads to poor performance for the different frac- tions of labeled data. Changing the color-jittrrngg from sample-wise to batch-wise slightly improves performances suggesting that even reducing the styles variability of augmented images helps for generalization. FA leads to drastic performance gains compared to the regular SimCLR which is surprising given their prior success in DG tasks. Combining SimCLR with BSS and batch-wise color-jitter yields even greater performance improvements and non-negligible gains compared to FA. This observation is also supported by results from Tables 1, 2, 3.
5.2 UNDERLYING MECHANISMS INVOLVED IN BSS EFFICIENCY
Spurious correlations reduction, harder negatives creation and reduced batch size require- ment (SimCLR). We hypothesized that BSS should help reduce the emergence of spurious corre- lations when repelling negatives from positives. Additionally, standardizing styles among positives and negatives should also facilitate the creation of harder negatives, known factor contributing to robust performance (Kalantidis et al., 2020; Robinson et al., 2020), while also reducing the demand for large batch sizes. To validate these hypotheses, we conducted 3 comprehensive experiments em- ploying SimCLR with standard augmentation, FA, or BSS on Camelyon1 7 WILDS: (1) To validate BSS's effectiveness in reducing spurious correlations, we computed the averaged domain purity for representations of unseen source and target examples after pretraining. This metric, which quantifies the degree to which each example and its nearest neighbors share the same domain label, serves as an indicator of the the impact BSS on encouraging the presence of harder negatives, we computed representations for several augmented batches and caleulated cosine similarities across all possible (anchor, neg- ative) pairs, reporting the values in a histogram (refer to Figure 2b). (3) Lastly, to assess BSS's s ability to mitigate the demand for large batch sizes, pretrained SimCLR varying batch sizes and assessed performance using linear probing (refer to Figure 2c). Figure 2a demonstrates that Sim- CLR with FA exhibits slightly lower average domain purity than standard SimCLR while SimCLR with BSS results in much lower average domain purity. This observation affirms BSS's effective- ness in attenuating spurious correlations and enhancing domain-invariance, Figure 2b indicates that standard SimCLR tends to produce negatives that are dissimilar to the anchors. SimCLR with FA produces negatives more similar to the anchors but not to the same extent as SimCLR with BSS which confirms BSS's role in creating harder negatives. Finally, Figure 2c reveals that standard SimCLR yields considerably lower performances compared to SimCLR with FA or with BSS. FA and BSS lead to improved performances for any batch size. As batch size increases, performances augment until a plateau is reached. However, when using BSS, this plateau is reached for lower batch size supporting BSS's is efficacy in reducing the need for large batches.
within SSL representations (refer to Figure 2a). (2) To assess
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95
0.95
SinCUR
0.50
SimCUR w/ FA SinCLR w/ BSS
Narber of mosetive
SmmCR
SinCCLR w/ FA
50-
Domain putts
SmCCLR w/ BSS
Accunay
86
SmCLR
E50000
R0
SmmCL w/
0.70
0.75
t0000>
SimCl w/ RS5
0.65
30003
70
0.60
65
0.55
250
500
1500 1750 2000 Noarest ragibors
150 1000
20.
-02 0.0
0.6 0.8
64
128
256
Casine similantyy (anchor, negative)
N
(a) Average domain purity varying (b) (Anchor, negative) cosine simi- number nearest neighbors
(c) Linear-
Linear-probing probing accuracy vary- ing batch size
larities
Figure 2: SimCLR experiments with standard augmentation, FA or BSS on Camelyon17 WILDS.
Better domain heterogeneity and class homogeneity for examples assigned to the same pro- totype (SWaV, MSN). We postulated that the coexistence of multiple domains/styles within views used for cluster assignments computation (i.e.: global views for SWaV and unmasked views for MSN) could introduce correlations between the assignments and domains/styles. To investigate the effectiveness of BSS in mitigating these correlations and shed light on why BSS yields su- perior representations compared FA, we conducted the following experiment: We pretrained backbone with SWaV using FA BSS DomainNet (sources: painting U real U sketch, tar- gets: clipart J infograph U quickdraw). During training, at every 1K optimization step, we com- puted representations from unseen source and target examples along with their hard assignments resulting from SK. Subsequently, we evaluated the homogeneity of representations assigned to each prototype in terms of domain class labels and averaged the homogeneity types. The evolution of the averaged homogeneity score with respect to domain or class labels are respectively reported in Figure 3a and Figure 3b. Figure 3a reveals that employing BSS instead of
over all proto-
0.275
SWSVV w/ FA
0.450
0.200
SW6VV w/ BSS
Homagenity so0e
0.400
0.578
0,000
0.255
0.505
SWAV w/ FA
SWVV w/ BSS
10000
20002
30000
40020 50003
0
10000
20000
50000 40020 50000
Optimization steps
Optimization steps
(a) Domain label
(b) Class label
Figure 3: Averaged homogeneity scores for representations assigned to the same prototype.
FA tends to reduce domain homogeneity (or improve domain heterogeneity) among representations assigned to the same prototype. This observation confirms BSS's role in reducing correlations be- tween assignments and domains. Conversely, Figure 3b illustrates that BSS results in higher class label homogeneity attesting that BSS helps to produce assignments more semantically coherent.
6 CONCLUSION
This work introduces Batch Styles Standardization, an image style standardization technique to be combined with existing SSL methods to address UDG. Extending existing SSL. methods with BSS offers serious advantages over prior UDG methods, including the elimination of domain labels and domain-specific network components dependencies to enhance inmainninvariance while offering versatility for integration. Leveraging BSS, the extended SSL methods exhibit improved general- ization capabilities, often surpassing or competing with alternative UDG strategies. Comprehensive experiments provide insights into the underlying mechanisms involved in BSS's efficiency. Other style transfer techniques, like GaN-based methods or AdaIN, could standardize image style to re- duce spurious correlations in SSL.. However, leave this exploration for future research.

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Published as a conference paper at ICLR 2024
7 ACKNOWLEDGEMENTS
This project was provided with computer and storage resources by GENCI at IDRIS thanks to the grant 101244001111224 on the supercomputer Jean Zay's V100 partition. This work was also partially supported by ANR-21-CE45-A gratitude to Spyros Gidaris and Enzo Ferrante for their valuable feedback and comments, which greatly contributed to the improvement of this research.
(Hagnodice). We would like to express our sincere
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A PSEUDO-CODE AND PYTORCH IMPLEMENTATION OF BATCH STYLES STANDARDIZATION
On Algorithm and Listing I, a pseudo-code along with PyTorch implementation of Batch Styles Standardization are provided. The full code will be released upon acceptance.
Algorithm 1: Batch Styles Standardization
Input
(Xi)isien: Batch of images
(Tmins T'ma)): ): Minimum and maximum area ratios between the substituted amplitudes components and the full amplitude.
Output: Batch of images with standardized style: (Xi)issNn
/f Computes Foueler transtorm foz Images the patch
for i + to N do
A(X;) Re (F(x)) Im (F(X,))
Im (F(X)))
P(X;) t arctan
Re (F(X.))
end for
Sample the associated style image sample amplitudes ares ratic ~ U[[1,..., ,N)
(Tming (UmminyTmaz)
// each image, substitute the fow-fueqynncy components with those the style image then apply the inverse Fourier transform to transfer the style onco the original image.
10 for i +- 1 to N do
A( +
X; - F-1 (A(x,)e-170) -P(x)) A(X;),
13 end for
14 return (XihsisN
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class
imppicnnnn Batch Styies Standardi Given batch of & insges ana their fourier transtoms, we nentpiclate the different anplitudes by cubstituting their iow-frecueney randonuy
det call (selt, imgs, n_viovel
"aepppy betch styles standerdizacion "n. vieve" times 385
image.
Args:
Astributes:
ings Etorch.Ionser. Batch inages (N) 3, 8, 20 (int)s Numoer augmentes vrexe
(tuplel: Sir,min, E_ specisying areas ratio betsoen the schstituted amplitude and the tull amplicude.
Acturns:
torch, Tensort Baton with standardizeo styles f_viows,
wiiiiiit_tsstt (sclf, zatios): celtiration ratio5
Apply FFT on source Imagez
ift torct. fttet
def cuastituto_ior wow_tregresst, *"rSuostitote Jow-trequency components of the souzco arplatudes with these of the target amplitudes.
din jov-trequency conponsacs
irt torch. torct. fft.
rit, dim [-2, -I1]
Retrieve amplicude
Args:
aпp, pazse fts.abs stc.angie(]
erc anp (torch. rensori: Source impittuses tgt_anp (corch, Tensori: Tazoet implitudes ratio (fleat) Area substituted amplitede the amplicude.
# Sample ALvioNs inages that wi11 be used as styles
(0)
sampled_ind torch, torch randparm(bi) [ia_views]
Sulocitute
ampl2tudes with
torch. Tensor: Scurco anclitudes whoze the tom-froqueney components have bosn subatituced with those
of ALvioNs sampled images
src_aro zmp unsqueeza repeatt
[I, n_vieks, 1, 3, :1)
amciltudes.
amp
uusequeaza .expand
-1, -1, -1)
Coapuce ceacer cocrdinates of amplitudes by 9 arc_ang shape
sanpled_rat
zandon, sandon,unifeent soniforn(sse...raee
anp self, wwwsritttte_ooooooooo.
ini
see_amp, Lg-_anp, seagled_
Conouco half lonoth 'I" of the componencs ia be subetituced
phase phase. unsqueesss
Reconaizact PPT fros and plase phase]
min(Iint teatioh/21, (sacio<w/221)
Ifc tozcl.
back los-froqueney a osiignal poait
iLate Zow freg components of sourcw amplicudes with those of the tazget amplitudes fre__reetttttom tet_aapf yos 1sherl, we 1sweril
IIc fft. it.. (Eft, dime(-2, -10)
inga torch. ifc.i
axc_ampl
fit, din- 1-2, -11) rea.........a.pp.. 1)
he- Ishell, Tiwell] 1ox_ Freg..gt....
raturn ings
Listing 1: Batch Styles Standardization PyTorch implementation
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B TECHNICAL DETAILS ABOUT SSL METHODS
B.1 SIMCLR
SimCLR aims bring representations of augmented views of the same image closer (positives) while repelling all other images representations (negatives). In practice, given a batch of N images, each image is augmented V times independently resulting
N V images grid where each row e corresponds to a content and each column to a view. For each image Xes and its corresponding representation Σzs E RR, SimCLR minimizes NT-Xent loss with temperature
Σ Les V
-1
log
(6)
N/ 8
(e",e")((Cca))
B.2 SWAV
SWaV computes the representations of different views of the same image while clustering them using online algorithm. Since representations should capture similar information, SWaV assumes that one view's ' cluster assignment can predicted from representations of other views. This swapped prediction idea the core concept behind SWaV loss formulation.
Concretely, in SWaV, an image X, augmented into views X and x with corresponding representations 2n
(*) and za Similarities between representations and K learnable cluster centroid- s/prototypes E Kx are computed and converted into probabilities such as follows:
C.
softmax
Vu E {s,t}
To compute cluster assignments also referred to as codes and denoted q SWaV relies on the Sinkhorn-Kin Koooonn--ppppp (SK) algorithm (Cuturi, 2013). SK performed on all views representations trying to assign representations to the most similar centroids but also uniformly among clusters. Finally, based on the swapped prediction concept, SWaV minimizes the following per-sample loss:
Cn = H (g (s) p + (( (1) p..))
(8)
In Equation 8, H (p, q) stands for the cross-entropy between approximated probability distribu- tion q and a true probability distribution q.
In practice, SWaV employs multi-crop strategy, generating global views (large crops) and V local views (small crops) for each image. Cluster assignments are then computed only from the global views while probabilities derived from all the V views. In this setting, SWaV minimizes the following loss:
V
Ln 2(V +
1 1sf H(gh)) .p.))
B.3 MSN
Given two views the same image, MSN randomly masks the patches of one view and leaves the other unchanged. Then, MSN's goal is to match the representation of the masked view with that of the unmasked view.
To derive view's representation, MSN computes the similarities between its embedding and set of cluster centroids/prototypes. subsequently transforming them into a probability distribution. As direct matching of these representations can lead to representation collapse, MSN simultaneously optimizes a cross-entropy term along with an entropy regularization term on the mean representation of the masked views. The entropy regularization term encourages the model to use the entire set of centroids/prototypess Additionally, MSN employs Sinkhorn Sinkhom-Klopp on the representations of the unmasked views avoid tuning the hyperparameter weighting the entropy regularization term.
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In practice and more formally, MSN generates for each image X,, M masked views (Xn.1.... X,.M) f and single unmasked view X Masked views are processed by a student encoder and the unmasked view by a teacher encoder whose weights updated via an exponential moving average of the student encoder's weights. Masked and unmasked views' embeddings de- of centroids/prototypes C Rxxx and the
noted (2n.1.-... En,M) 2 then compared resulting similarities are converted into probability distributions {pr.1.... .Pp... P....
Zn.m
Pn.m softmax
CT
(10)
p softmax
T
and y+ stand for temperature hyperparameters and chosen such that x+ to encourage sharper probability distributions implicitly guiding the model to produce confident masked views representations. Given a batch of N images, MSN minimizes the following loss:
M
1
C
Σ
H(P Pm,m) - (H
NM
m=l
(11)
N
M
Σ
Pm,m
NM
n=
m==
(p, q) stands for the cross-entropy between an approximated probability distribution q and true probability distribution while (f) denotes the entropy of the masked views' mean representation p.
C IMPLEMENTATION DETAILS
C.I PRETRAINING
On PACS and DomainNet, as part of the geometric augmentations, we use random crop resizing, horizontal flips, small rotations, cutout(DeVries & Taylor, 2017) while color augmentations are ap- plied in batch-wise manner using color jitter, random equalize, random posterize, random solarize and random grayscale. On Camelyon17 WILDS, we use random crop resizing, flips, rotations, cutout and batch-wise color jitter. All other hyperparameters for SimCLR, SWaV, MSN are respec- tively specified on Tables 5, 6, 7.
Table Hyperparameters used for SimCLR extension based on Batch Styles Standardization
datasets
PACS
DomainNet
Camelyon17 WILDS
backhone
ResNet-18
ResNer-18
ResNer-50
2242
1282
2242
128"
12g2
("min: Fmazs
(0.02, 1)
(0.02,
(0.02, 0.1)
D
128
128
128
0.5
N
256
512
256
steps optimizer learning rate learning rate schedule weight decay
60K
60K
150K
LARS
LARS
0.2
0.4
0.2
linear warmp-up + cosine docay
lincar warmp-up + cosine decay 10-5
lincar warmp-up cosine decay
10
10-6
C.2 FINE-TUNING/LINEA-PRRR PROBING
For datasets (PACS, DomainNet, and Camelyon17 WILDS), we use the Adam optimization method (Kingma & Ba, 2014) with an initial learning rate of 10-4, learning rate scheduler with cosine decay, and weight decay of 10 44 The networks are trained respectively for 5K, 1K. and 15K steps with batch sizes of 128, 64, and 64. When performing linear probing. follow the same normalization scheme as (He et al., 2022) by adding a batch normalization layer (loffe Szegedy, 2015) without affine parameters before the linear classifier.
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Table 6: Hyperparameters used for SWaV extension based on Batch Styles Standardization
datascts
PACS
DomainNet
Camelyon17 WILDS
backbone
ResNet-18
ResNet-18
ResNet-50
global views
224"
224"
128"
local views
6 128"
128"
6 x 128
(7 Mmim )
(0.02, 1]
(0.02. 1)
(0.02, 0.1)
256
256
256
D
128
128
128
0.1
0.1
0.1
256
steps optimizer learning rate learning schedule weight decay
60K
6UK
150K
LARS
LARS
LARS
0.2
0.4
lineur warmp-up cosine decuy
lineur warmp-up decuy
lineur warmp-up cosine decay
10
10
10
Table 7: Hyperparameters used for MSN extension based on Batch Styles Standardization
backbone unmasked views
VTT-S/8
2 962
masked views
10 x 64
(Tmin I'max)
(0.02,
patch masking ratio
0.3
K
128
D
384
0.1
++
0.025
N
256
steps Optimizer
150K
LARS
learning rate
0.2
learning rate schedule
linear warm-up cosine decay
weight decay
10
EMA momentum
0.995
D DISCUSSIONS & ADDITIONAL VISUALIZATIONS
D.1 TRANSFER LEARNING DG/UDG
The usage of Transfer Learning in DG/UDG is common but think it is misguided. Often the pretraining dataset, such as Imagenet, can include one or more of the target domains, e.g., photo for PACS or real for DomainNet. When evaluating on these domains, performances result from the generalization ability of the DG/UDG methods or from the transfer learning. For new DG/UDG methods, it is hard learning unfairly boosts the results of previous works, and state-of-the-an performances are often seen prerequisite for paper acceptance. We have tried to limit the usage of transfer learning in our experiments and only used it for DomainNet.
possible to know
follow the common practice because transfer
To demonstrate that UDG methods can unfairly benefit from ImageNet transfer learning. we have trained SimCLR with BSS both with and without ImageNet transfer learning. The resulting perfor- mances are reported Table 8. These results clearly show that ImageNet transfer learning leads to substantial improvement in UDG performance across all label fractions and for nearly all target domains.
D.2 FEATURES VISUALIZATION
To assess the quality of the SSL representations and their ability generalize across domains, display, in Figure 4, t-SNE plots of the backbone representations for SimCLR with BSS and com- petitors Camelyon17 WILDS. DARLING representations tend to be domain-invarian as lots of examples from different domains are superimposed. However, this also the case for many exam- ples from different classes indicating potentially poor model generalization. In contrast, DiMAE
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Table 8: Impact of Imagenet transfer learning on UDG performances for PACS. Best methods are highlighted in bold. and + stand for absolute gain or loss of performances when using Imagenet transfer learning.
Mathoals
planto
Cortion
Earpet dimman
DVg.
APV
Cortion
Eargat damain
skminl
DIMAD!
$1.73
25.83
$2.50
$4.23
50000
41:25
30,40
R0.9
BrAD"
61.81
33.57
43.47
36.37
43,80
65.22
41.35
50.38
50.65
52.03
CycleMAR::
52.63
$6.35
95.59
34.85
99.83
63.24
50.06
42.15
36.
45.41
SInCLR or BSS
43.51
39,96
48.61
48.76
$4.91
55.16
46.37
55.69
65.61
55.00
SICCLR oi BSS
42.04 tL 1.87)
42.84
<T 3.58)
55.37
0.70)
$340 IT 4.051
48.43
(1 5.533
63.59
(1 5:43)
53465
(1 7:28) 62/06 6.87) 02.81 (1 9.58)
60.53
4:38)
Fraction
Mathods
Targer
Lurger
DIMAE
57.72
38.00
63:23
BrAD
72.17
44.30
50.01
55.66
55.51
X
CycleMAE
67.93
29.34
38.25
A2.87
90.72
75:34
69.33
50.24
71.41
SinCLR
60.04
62.73
73,02
Uses Imagenet transfer learning.
DARLING
DIMAE
Ours
hospital_ o
hospital_ 3
hospital .4
Dr hospital _
hospital 2
y tumor healthy
Figure 4: I-SNE plots of the backbone representations for different UDG methods on Camelyon17 WILDS. Colors and markers correspond respectively to different domains and classes. On the tar- get domains (hospital,, hospital.2) our method (SimCLR w/ BSS) shows better domain confusion while keeping better class separability. Zoom on pdf for better visualization.
representations appear to be well separated by classes but also by domains, especially for the target domains cospitall and hospi cooppiall. indicating lack of domain-invariance Finally, bet- ter class separability and domain confusion emerge from the representations of SimCLR with BSS revealing better Komain-invariance and potentially better cross-domain generalization.
D.3 IMPACT OF ON BSS GENERATED IMAGES
To illustrate the effect of the hyperparameter on the generated images by BSS, we apply BSS on a single batch fixing the chosen style image and varying r. The resulting images are reported in Figure 5. We can observe that as increases, textures/styles with higher frequencies are transferred to the resulting images.
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Raw batch
Style img
Standardized batches with BSS
8
0.1
0.2
0.3
r
Figure 5: Impact of hyperparameter r on augmented images with BSS
19


Reviews:
[{"review_id": "tZdtRlTon0", "reviewer": "Reviewer_qVbh", "summary": "This paper proposes Batch Styles Standardization (BSS) to reduce spurious correlations in conventional self-supervised learning (SSL) representations, thereby making the resulting models generalize better on the test data drawn from an unseen domain. Specifically, the authors leverage the existing Fourier-based augmentation technique to transfer the style of a randomly chosen image to all other images within a batch. They also elaborate how BSS can be integrated with popular contrastive and non-contrastive SSL methods such as SimCLR, SWaV, and MSN. Experiments were conducted on 3 benchmark datasets for domain generalization to evaluate how BSS improves the performance of these SSL methods.", "questions": "Can authors conduct some investigation on why BSS is sometimes outperformed (though by a small margin) by regular Fourier-based augmentation on the DomainNet dataset?", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. The paper is well written.\n2. The authors perform a comprehensive literature review on SSL and unsupervised domain generalization.\n3. The paper offers a clear explanation of how Fourier-based augmentation and BSS operate on images, making the methodology more reader-friendly for a broader audience.", "weaknesses": "For now I do not see any obvious weaknesses or technical flaws in the paper. However, it would be beneficial if the authors could provide further clarity on the novelty aspect. At the moment, it appears to be an application of Fourier-based augmentation to self-supervised learning.\n\nMinor suggestions:\nSimCLR and SWaV should be categorized as contrastive-based SSL methods in the second paragraph of Contributions.", "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": "nvR4llSAvR", "reviewer": "Reviewer_eMsd", "summary": "This paper proposes a batch-standardization method for domain-invariant self-supervised learning. The idea is borrowed mainly from Fourier domain adaptation. However, the new advantage is that it eliminates the requirements of domain labels. This paper validates the effectiveness on various benchmarks, such as PACS, DomainNet.", "questions": "Novelty and experiments are my most important concerns. Please carefully address my concerns listed in the weakness.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "The biggest advantage of the proposed method is that it does not need domain labels to learn domain invariant features. It indeed reduces the requirements for domain labels. The experimental results show the effectiveness of the proposed BSS on various benchmarks.", "weaknesses": "[1] Originality: This paper proposes a batch-style standardization method to mix the domain styles in the batch. However, the idea is largely borrowed from Fourier Domain Adaptation [A], FACT[B] and Domain-invariant masked autoencoder [C]. The extension to samples in a mini-batch is also direct and does not need significant designs. Considering the author only claims one novelty, I do not think this paper is above the bar of ICLR.\n\n[A] FDA: Fourier Domain Adaptation for Semantic Segmentation\n[B] A Fourier-based Framework for Domain Generalization\n[C] Domain Invariant Masked Autoencoders for Self-supervised Learning from Multi-domains\n\n\n[2] The experimental results. I have also noticed that CycleMAE, which was published in the last ICLR, also lists the comparison of different pretrained models in Table 5. It shows comparable results with unsupervised learning pretrained models. In addition, the author should compare with CycleMAE [D].\n[D] CYCLE-CONSISTENT MASKED AUTOENCODER FOR UNSUPERVISED DOMAIN GENERALIZATION", "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": "qkf7sxgnmM", "reviewer": "Reviewer_gWCx", "summary": "This paper studies the unsupervised domain generalization problem where there is a labeled training set, an unlabeled training set, and a test set. The main claim is that the style information should be standardized in training, which motivates the authors to propose BSS (Batch Style Standardization) to combine with self-supervised learning methods. Experiments on several benchmark datasets show the effectiveness of the BSS method.\n\n-----Post rebuttal\n\nI increased my score from 3 to 5 since they addressed my concerns. However, I did not give a 6 since I still think the novelty is limited.", "questions": "See weakness.", "limitations": "", "rating": 5, "confidence": 5, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. The proposed method is simple and useful in image datasets.\n2. The combination of BSS with SSL is interesting.\n3. The results show that the BSS approach is effective compared to other counterparts.", "weaknesses": "1. The major weakness is novelty. Given that Fourier-based methods have been extensively studied in existing DG literatures, the direct adoption of Fourier features is not entirely novel. Plus, the idea of transforming the styles in batch is deeply related to Mixstyle [Zhou et al., ICLR21] but authors did not compare or discuss the difference.\n2. The motivation of combining BSS with self-supervised learning is not clear. I can only see this: we can always combine them, that's all. I do not see the insights of such combination.\n3. Section 3.2, i.e., the BSS part, is hard to understand. Authors should do their best to better present this part.\n4. Comparision approaches are not enough: authors should compare with existing Fourier methods to validate their effectiveness.\n5. There lacks theoretical support of why such BSS approach can succeed in learning domain-invariant representations.", "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": "ccx2PtNpMP", "reviewer": "Reviewer_PTUx", "summary": "The paper studies the problem of Unsupervised Domain Generalization (UDG) and proposes Batch Styles Standardization (BSS) for contrastive-based pretraining. It is a Fourier-based method that aims to standardize the style of images in a batch. The method can be plugged into many existing methods easily and shows good performance improvement across multiple benchmarks", "questions": "- What are the source domains for PACS pretraining?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "- The paper is well-written and easy to follow.\n\n- The motivation is clear and the method is simple and neat.\n\n- The reported performance improvement is significant over the previous methods.", "weaknesses": "My major concern is that some important baselines are missing:\n- What is the performance of the ERM (empirical risk minimization) on those benchmarks? People have observed ERM being a very strong baseline when it comes to domain generalization settings [1, 2].\n- As a small fraction of labeled data is always used, why not try some good semi-supervised learning methods such as FixMatch [3] or AdaMatch [4] (which also deals with domain shift)? And there is also contrastive-based semi-supervised learning such as CoMatch [5]. Since the goal is to improve the performance on unseen target domains, what is the advantage of using a framework of unsupervised pretraining + finetuning?\n- A related remark would be: what is the SOTA methodology when it comes to domain shift? In practice, one may easily resort to some VLMs (e.g. CLIP and its variants) pre-trained on large-scale image-text pair data when facing domain shift problems. As CLIP models have shown very good performance on samples under distribution shift, especially in image classification, I wonder to what extent can the problem be solved by them already. I think it makes more sense to develop techniques on top of these strong baselines, as it is very likely the method or the performance improvement on small-scale datasets or non-SOTA models does not transfer/scale well. It would be much more interesting to see if the proposed BSS still holds the same performance gap on top of CLIP. That being said, this remark is not a criticism of the authors who follow the common practice. But it would be still great if the authors could share their thoughts on this point.\n\nMinor:\n- To be more self-contained, it would be great if the authors could also introduce the contrastive-based UDG methods before Section 3.2.2.\n\n[1] In Search of Lost Domain Generalization, Ishaan Gulrajani et al., ICLR 2021\n[2] OoD-Bench: Quantifying and Understanding Two Dimensions of Out-of-Distribution Generalization, Nanyang Ye et al., CVPR2022\n[3] FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence, Kihyuk Sohn et al., NeurIPS 2020\n[4] AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation, David Berthelot et al., ICLR 2022\n[5] CoMatch: Semi-supervised Learning with Contrastive Graph Regularization, Junnan Li et al., ICCV 201", "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": "sdrTfQtWEu", "reviewer": "Reviewer_qeEH", "summary": "Existing UDG ( Unsupervised Domain Generalization) methods usually require samples to have domain labels for better learning of domain invariant features. The collection of domain labels is also costly in practical scenarios, which limits existing UDG. This paper proposes a BSS ( Batch Styles Standardization) approach in combination with existing SSL ( Self Supervised Learning ) methods, eliminating the need for domain labels. The authors combine the proposed BSS method with several SSL methods, experiment on UDG datasets, and obtain significant improvement in results.", "questions": "1. What does \"spurious correlations\" mean? Is it the possibility that in SSL, when repelling negative samples, the main basis for error may be the difference in style?\n2. In section 4.2, \"We did not use ImageNet transfer learning, except on DomainNet to allow fair comparisons with prior UDG works.\" Does it mean that this paper uses an ImageNet pre-trained model when conducting experiments on the DomainNet dataset?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "1. This paper studies a novel problem, i.e. Unsupervised Domain Generalization without domain label. The basic idea is sound and very worthwhile.\n2. Compared with the existing UDG methods, the experimental accuracy of the author's method has a significant advantage.\n3. The writing is good, and the structure is easy to follow.\n4. The ablation experiment was adequate.\n5. In the part of comparative experiment, the author combined BSS with various types of SSL methods to demonstrate the universality of this method.", "weaknesses": "1. Inadequate ablation experiments.\n\nIn Section 5.2, the authors do not show the ablation effect of the original SimCLR. Table 4 demonstrates the effectiveness of FA and BSS compared to baseline SimCLR. However, it is not reflected in Section 5.2. So it does not show that FA and BSS are effective compared to \noriginal SimCLR.\n\n2. Perhaps there is a limitation to BSS.\n\nAssuming that domain labels are not available, there is no guarantee that a number of randomly selected images belong to different domains. As the author said, \"Finally, after applying the inverse Fourier transform to the different modified Fourier transforms, the style of the randomly chosen image is transferred to all images, effectively standardizing/harmonizing the style.\"\nIn the worst case, it is possible that a number of randomly selected images all belong to the same domain (same style), and then their magnitude spectra may be similar. If the magnitude spectra of these images are still used to augment all of the images, it may indeed create harder negative samples (because of the similar magnitude spectra). However, it would also create the problem of more similarity between pairs of positive samples, potentially forcing the network to capture more similar style information than domain invariant information in the positive sample pairs.", "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)