In Search of Forgotten Domain Generalization
Authors: Prasanna Mayilvahanan
Roland S. Zimmermann
Thaddäus Wiedemer
Evgenia Rusak
Attila Juhos
Matthias Bethge
Wieland Brendel
Venue: ICLR 2025 Spotlight (2025)
Keywords: Out-of-Distribution Robustness
OOD generalization
Out-of-Domain Robustness
Evaluation
Primary area: foundation or frontier models, including LLMs
TL;DR: CLIP's high performance on style-centric domain shifts is significantly influenced by the presence of such images in its training set.

Abstract: Out-of-Domain (OOD) generalization is the ability of a model trained on one or more domains to generalize to unseen domains. In the ImageNet era of computer vision, evaluation sets for measuring a model's OOD performance were designed to be strictly OOD with respect to style. However, the emergence of foundation models and expansive web-scale datasets has obfuscated this evaluation process, as datasets cover a broad range of domains and risk test domain contamination. In search of the forgotten domain generalization, we create large-scale datasets subsampled from LAION---LAION-Natural and LAION-Rendition---that are strictly OOD to corresponding ImageNet and DomainNet test sets in terms of style. Training CLIP models on these datasets reveals that a significant portion of their performance is explained by in-domain examples. This indicates that the OOD generalization challenges from the ImageNet era still prevail and that training on web-scale data merely creates the illusion of OOD generalization. Furthermore, through a systematic exploration of combining natural and rendition datasets in varying proportions, we identify optimal mixing ratios for model generalization across these domains. Our datasets and results re-enable meaningful assessment of OOD robustness at scale---a crucial prerequisite for improving model robustness.

Published as a conference paper at ICLR 2025
IN SEARCH OF FORGOTTEN DOMAIN GENERALIZATION
Prasanna Mayilvahanan 1,2,3-
Roland S. Zimmermann' 1.2.3*
Thaddäus Wiedemer 1.233
Evgenia Rusak 1,2,3
Attila Juhos 1.2.3
Matthias Bethge
Wieland Brende1
ABSTRACT
Out-of-Domain (OOD) generalization is the ability of model trained on one more domains to generalize to unseen domains. In the ImageNet era of com- puter vision, evaluation sets for measuring a model's OOD performance were designed to be strictly OOD with respect to style. However, the emergence of foundation models and expansive web-scale datasets has obfuscated this evaluation process, datasets cover broad range of domains and risk test domain contam- ination. In search of the forgotten domain generalization, we create large-scale datasets subsampled from LAION-LATON-Natte are strictly OOD to corresponding ImageNet and DomainNet test sets in terms style. Training CLIP models these datasets reveals that significant portion of their performance is explained by in-domain examples. This indicates that the OOD generalization challenges from the ImageNet era still prevail and that training on web-scale data merely creates the illusion of OOD generalization. Furthermore, through a systematic exploration of combining natural and rendition datasets in varying proportions, we identify optimal mixing ratios for model generalization across these domains. Our datasets and results re-enable meaningful assessment of OOD robustness at scale crucial prerequisite for improving model robustness.
and LATON-Rendition that
A ImageNet Era
Web-Scale Era
C Web-Scale + Filter
Domain Acouracy before/After
Training
ImageNNt
Traning
LAION
Training
-LAION
Rendhtin Natural
Treined in B
Trained C
Natural Domain
IN-S
IN-R
fitter
II-S
IN-R
Trained
Evaluation
IN-S
IN-R
Evaluation
Evalustion
Trained C
Clipert
Clipert
Rendition Domain
0.0
0.1
0.2
0.3
0.4
0.5
Models are not robust
Models appear robust
Models turn out not robust
Figure 1: Evaluated correctly, CLIP's OOD performance renditions drops significantly.
A: Models used to be trained on a single domain like natural images from ImageNet (Russakovsky al.. 2015) and evaluated for out-of-domain (OOD) generalization on a different domain like renditions from test sets such as ImageNet-R (Hendrycks et al.. 2021a), ImageNet-Sketedl (Wang al., 2019). Today, large foundation models like CL.P (Radford et al., 2021) are trained on web-scale datasets such as LAION-400M (Schuhmann et al., 2021) containing images from many domains. Tested on a specific domain like renditions, CLIP exhibits unprecedented performance and appears robust. C: We subsample from deduplicated LAION-400M (Abbas et al., 2023) to obtain LAION-Natural, a web-scale dataset containing only natural images, which re-enables a meaningful assessment of CLIP's generalization performance to renditions. D: CLIP trained LAION-Natural performs noticeably poorer on renditions, suggesting that its OOD performance has been previously overestimated. The models are evaluated refined test datasets containing samples only from their intended domains.
'Equal contribution. 'University of Tübingen, "Tübingen AI Center, "Max-Planck-Insiine for Intelli- gent Systems, Tübingen, "ELLIS Institute Tübingen. Contact: prasanna.may mayilvahaaaa @uni-tuebingen.de, research sssarc@@zinmermaan.comm Code available https: hotsss//beeneeeeee group githup womuu.io//ee ht/cllppc///////

---

Published as a conference paper at ICLR 2025
INTRODUCTION
Foundation models have revolutionized our world, demonstrating remarkable capabilities in solving grade school math problems, writing creative essays, generating stunning images, and comprehending visual content (OpenAT, 2023; Schulman et al., 2022; Ramesh et al., 2022). One notable example is CLIP (Radford et al., 2021), vision-language model pretrained on vast dataset of image-text pairs, which forms the backbone of numerous other foundation models (Ramesh et al., 2022; Liu al., 2023a). CLIP has achieved unprecedented performance across a wide range of benchmarks spanning many domains-a sharp contrast to models from the ImageNet era, which struggled to generalize from a training domain mostly consisting of natural photographs to stylistically different domains such as ImageNet-Sketell (Wang et al., 2019), ImageNet-R (Hendryeks et al., 2021a), and DomainNet (Peng et al., 2019).
Domains, while often challenging to quantify in practice (Ben-David et al., 2010), emerge from collecting data from specific sources and conditions. Some domains, like natural images or renditions, are better delineated, allowing the creation of datasets like the ones mentioned above. Out-of-domain (OOD) generalization refers to a model's s ability to perform well on data from domains other than its training domain(s) (Wang et al., 2021). In this work, we collectively refer the domain represented by ImageNet- Sketch, ImageNet-R, as the rendition domain, since it contains images that are renditions of
CamaainNet-Painting
DommainNet-Clipart, DomainNet- Sketth,,
and MaanaiinNe-Quucckdeee natural objects and scenes. Generalization to the rendition domain (especially OOD) is crucial for aligning models with human perception, humans interpret abstract visual renditions, while machines tend to rely heavily on textural cues (Hendrycks et al., 202 Geirhos et al., 2019).
CLIP's strong performance in several domains, including renditions, is attributed its vast training distribution, rather than its contrastive learning objective, language supervision, or dataset size (Fang al., 2022). However, Fang et al. (2022) do not specify what characteristics of the training distribution drive this performance. CLIP could be learning more robust representations due to the diversity of natural images in its training set-or may simply have been exposed to many datapoints from the (assumed to be OOD) test domains during training. Indeed, Mayilvahanan et al. (2023) revealed that CLIP's training data contains exact or near duplicates of samples of many OOD datasets. Yet, they showed that CLIP still generalizes well when this sample contamination is corrected. However, their analysis failed to account for domain contamination.
In contrast to sample contamination, domain contamination does not focus on duplicates of specific datapoints but rather examines whether critical aspects of a test domain are present in the training domain, such as images with different content but similar style to test samples. For example, after the correction by Mayilvahanan et al. (2023), many other rendition images, while not duplicates, remained in CLIP's training set (refer Tab. 2). Prior works often assume that CLIP is capable of generalizing OOD (Radford et al., 2021; Abbasi et al., 2024; Nguyen et al., 2024; Fang et al., 2022: Li al., 2023; Shu al., 2023); however, it remains unclear whether this is truly the case if its performance is primarily driven by training on images from the test domain. This leads us to our central question:
To what extent does domain contamination explain CLIP's performance on renditions?
We address the central question with the following contributions:
To rigorously test whether CLIP's success in the rendition domain stems from their exposure during training, we first train a domain classifier distinguish natural images from renditions (Sec. 3.2). By applying the domain classifier to a deduplicated version of LAION-400M, we create and release two datasets: LAION-Natur contains 57 M natural images; LAION-Rendition consists of 16 M renditions of scenes and objects. Additionally, we refine existing rendition OOD benchmarks (ImageNet-R, ImageNet-Sketcch etc.) by removing samples that do not belong to the corresponding domain (Sec. 3.4).
Constructing Clean Single-Domain Datasets:
Refining the Evaluation of CLIP's OOD Performance: Using LAION LAION-Natural Natural, we demon- strate that CLIP trained only on natural images significantly underperforms rendition domain shifts (Sec. 4). This suggests that its original success stems from domain contamination, not from intrinsic OOD generalization ability (see Fig. 1 for summary).
2

---

Published as a conference paper at ICLR 2025
Investigating Domain Mixing and Scaling Effects: Our single- domain datasets enable analyzing the effects of training on controlled mixtures of natural and rendition images across scales (Sec. 5). We identify the optimal mixing ratio for the best overall performance and show the degree to which training on one domain enables some generalization to the other.
Through this work, we aim to shed light on the limitations of foundation models like CLIP in handling OOD generalization and provide valuable datasets and tools to the community for further exploration. Fig. illustrates our core methodology.
2 RELATED WORK
Measuring the OOD Generalization of CLIP Models We aim to understand the OOD gener- alization capabilities of CLIP from data-centric viewpoint. While multi-modal training with rich language captions does seem to contribute to robustness against distribution shifts (Xue et al., 2024), Fang et al. (2022) demonstrated that the nature of CLIP's training distribution (as opposed to its mere size, its specific training objective, or natural language supervision) causes strong performance on various distribution shifts. However, it is unclear what aspects of the data distribution drive the robustness gains. Mayilvahanan et al. (2023) remove images highly similar to the test sets to show that data contamination and high perceptual similarity between training and test data do not explain generalization performance. While their data pruning technique removes some samples from LAION-400M that lie outside the natural image domain, they do not address domain generalization: They only account for the part of domain covered by existing test sets and give guarantee that all images of a given domain were removed. In another line of work, Nguyen et al. (2022) discover that a model's effective robustness (Fang al., 2022; Taori et al., 2020) on test interpolates when training data is compiled from various sources. However, they only consider mixing datasets that each cover multiple domains. In this work, we take their analysis further and show how mixing two data sources from distinct domains interpolates the effective robustness on those domains. Our study's 's title is inspired by Gulrajani Lopez- (2021), who studied generalization from multiple distinct source domains. In contrast, we focus on generalization from single or mixed source domains to unseen domains. Overall, we aim for our work to be a valuable addition to the literature on OOD generalization (Liu et al., 2023b; Koh et al., 2021; Madan et al., 2021; Gulrajani & Lopez-Paz, 2021; Madan et al., 2022; Arjovsky et al., 2019; Arjovsky, 2021).
Domain Classification The primary goal of our work necessitates creating web-scale datasets of different domains. This entails building a robust domain classifier that can reliably distinguish natural images from renditions. This task can be regarded as classifying the style of an image, which Gatys et al. (2015) proposed measure using Gram matrices and which has been widely explored since then (Sandoval et al., 2019; Menis-Mastromichalakis et al., 2020; Sandoval Rodriguez et al., 2018; Joshi et al., 2020; Garcia & Vogiatzis, 2018; Chu & Wu, 2018; Bai et al., 2021). More recently, Cohen-Wann Wang et al. (2024a) use a fine-tuned CLIP model from OpenCLIP (Ilharco et al., 2021) to distinguish between ImageNet and test sets with a domain shift, such as ImageNet-Sketch ImageNet-R, and ImageNet- V2 (Recht et al., 2019). Wang et al. (2023) and Somepalli et al. (2024) develop a dataset classifier using a backbone trained by self-supervised learning and classification through retrieval via a database. Liu & He (2024) report high performance when training image classifiers to distinguish between different large-scale and diverse datasets.
CONSTRUCTING CLEAN SINGLE-DOMANN DATASETS
To answer our central question how much of CLIP's performance on renditions can explained by domain contamination- we must filter out datapoints from specific domains within web-scale datasets. Similar to how ImageNet compared to ImageNet-Sketch and ImageNet-R, or how DomainNet-Real is compared to DomainNet-Sketch (Quickdraw, Infograph, Clipart, and Painting), we aim to create clean natural and rendition datasets from LAION by building a domain classifier to distinguish between these domains. To build robust domain classifier, first create labeled dataset where each class represents a distinct domain. The labeling process is outlined in Sec. 3.1, and explore different ways build domain classifier in Sec. 3.2. Further, in Sec. 3.3, employ the best-performing classifiers to analyze the composition of different training and test sets and finally in Sec. 3.4.
use subsample LATON-Natural and
3

---

Published as a conference paper at ICLR 2025
LAION-200M
ImageNet Test Sets
DomainNet Test Sets
Rendition Ambiguous Natural
MURTEN GRANAU
Figure 2: Labeled natural, ambiguous, and rendition samples from different datasets. Natural images are photos high-quality renders with minor filters that preserve fine- textures, while renditions are typically sketches, paintings, or graphics with flat or simplified textures. Images with elements of both, such as collages or natural images with large stylized elements, and images mainly containing text are labeled as ambignous.
LAION-200M For the remainder of this work, we substitute LAION-400M with AION-200M, which we obtain by de-duplicating LAION-400M based perceptual similarity introduced by Abbas et al. (2023). Both Abbas et al. (2023) and Mayilvahanan et al. (2023) demonstrate that CLIP trained on LAION-200M obtains comparable downstream performance while greatly reducing the computational burden of training models from scratch and analyzing the dataset.
3.1 LABELING
LAION-200M contains diverse images from a multitude of sources. The images vary from naturally occurring to synthetically generated. We encourage the reader to glance at Fig. 20 to get sense of the dataset and the difficulty of determining the domain of each image. We aim to classify these images mainly natural renditions. We also add extra ambiguous class for images with elements of both domains, images with elements neither, and edge cases.
We manually label images based on labeling handbook derived from analyzing the existing OOD test sets, which we outline in Appx. A.1.1. In general, we adopt texture-centric approach to distinguish renditions of a scene or object from their natural depictions. That is, depictions where grained texture information is preserved are generally considered natural, while depictions with simplified flat textures are considered renditions. Fig. 2 illustrates this demarcation samples from LAION-200M, ImageNet test sets, and DomainNet test sets.
To further ease the labeling procedure, we first build rough binary classifier by fine-tuning CLIP VIT- L/14 with a linear readout to differentiate between some of the natural ImageNet and DomainNer test sets (namely, Image Val, ObjectNet (Barbu et al., 2019), ImageNet-V2, ImageNet-A (Hendrycks et al., 2021b), and DomainNet-Redd and rendition test sets (namely. ImageNet-Sketee ImageNet-R, R, NomainNet--aintinn
and DomainNet-CCipar)) We use this classifier roughly pre-label samples before they are annotated by a human. The annotator verifies and potentially updates the labels for 25 images at time (see Fig. 7).
DomainNet-Skett
Overall, we label 19 000 random images from LAION 200M and 1000 images from each of the ImageNet and DomainNet test sets (12 000 in total). Notably, almost all ImageNet and DomainNet test sets usually assumed to contain only images of a single domain exhibit some domain contamination. We discuss this in detail Sec. 3.3. Tab. contains detailed breakdown of labels for each dataset. We show more samples grouped by domain for each dataset in Figs. 23 and 34.
3.2 TRAINING AND CHOOSING THE DOMAIN CLASSIFIER
With the
dataset, we can train a domain classifier to partition all of LAION-200M into natural images, renditions, or ambiguous images. Since aim to obtain datasets containing
4

---

Published as a conference paper at ICLR 2025
Table We chose the best natural classfier and the best rendition classifier between a binary classifier based on (DR) (Cohen-Wanng et al., 2024b) and a ternary classifier using a linear readout based on fine-tuned CLIP model (FT). All models use CLIP ViT-L/14 pretrained on LAION-2B. We report precision and recall for the natural class (top) and rendition class (bottom) on ImageNet (IN) and NomainNet (DN) test sets and average performance across all test sets, For each class, we select the classifier with the highest validation-recall.
cls cls-natural
Val
Test
IN-Val
IN-22
IN-A
ON
DN-R
Average P
Model
P
P
R
R
R
R
P
DR-R
0.98 0.08 0.72
0.08
1,00
0.00
1.00
0.00
1.00
0.00
0.95
0.20
1.00
0.00
0.95
0.05
0.98
0.41
0.95
0.44
1.00
0.36
0.40
1.00
0.46
0.99
0.53
1.00
0.42
0.99 0.43
cia-rendition
Val
Test
IN-R
IN-S
DN-S
DN-Q
DN-P
DN-C
DN-1
Average
Model
P
P
R
R
R
P
P
DR-R
0.98 0.35
0.98
0.41
1.00 0.60 1.00
1.00 0.74 1.00 0.33 0.99 0.60
1.00
0.65
0.98 0,99 0.53
FT
0.98 0.27 0.95
0.26
1.00 0.38 1.00
0.57
1.00
0.61 1.00 0.68 1.00
0.21
1,00
0.50
1.00
0.30
0.99 0.42
only images from single domain, we need a domain classifier that as precise as possible. To this end, we train classifiers on 13 000 labeled LAION-20OM images, retaining 3000 samples each for validation and test set. From the domain classification literature discussed in Sec. 2, we evaluate four methods with publicly available code. All methods build on CLIP ViT-L/14 pretrained on LAION-2B, which we choose for balance between accuracy and inference speed. For brevity, we present the two methods we finally employ here, and refer the reader to Appx. A.1.2 for a detailed description, results, and comparisons with other approaches.
Density Ratios Cohen- Wang et al. (2024b) aim to estimate the probability that a given sample is drawn from reference distribution Pret- Since high dimensional density estimation is challenging, they build a classifier to distinguish between reference and a shifted distribution and compute the density ratio Pat which they threshold 0.2 to classify a given sample. We deploy their method unchanged to our task. We obtain two binary classifiers, DR-N and DR-R, that distinguish natural from non not-natural samples and renditions from non-renditions, respectively.
Paines
Fine-Tuning We fine-tune pretrained CLIP ViT-L/14's image encoder with a randomly initialized linear readout on the training dataset to obtain a ternary classifier, dubbed FT.
We use the validation set to determine the two best domain classifiers, one for natural images and one for renditions. Since the domain classifier should maximize precision above all else, we set the confidence threshold for each model such that it achieves 98 % per-class precision. We then pick the classifier with the highest per-class recall to minimize the number of datapoints that are discarded when subsampling LAION-200M to build LATON-Natural and LAION- MON-Rendition. We choose FT, the tune- ternary classifier, and DR-R, the binary classifier using density ratios, to detect natural and rendition images, respectively. We use these classifiers for all subsequent experiments. Tab. I reports these models' precision and recall on the natural and rendition class across ImageNet and DomainNet test sets. For comparison to other methods see Appx. A.1.2. For raw accuracy numbers of all models, which in general are high for most, refer to Tabs. 7 and in Appx. A.1.5. We also assess the quality of the labels and the domain classifier's predictions in Appx. A.1.4, finding them to be robust even in the presence of label noise during training.
3.3 ANALYZING THE DOMAIN MAKE- UP OF DIFFERENT DATASETS
Both ImageNet and DomainNet are web-scraped datasets that were refined through extensive human annotation. In contrast, LAION-400M is obtained purely through web scraping without subsequent human domain filtering. Since human annotators can make mistakes, and LAION- domain domain classifiers understand it.
composition is inherently unknown,
To this end, we deploy the chosen classifiers from Sec. 3.2 and label a sample ambiguous the natural and rendition classifier disagree. We apply the classifiers both with their strict thresholds at 98 % validation validation-precisionn, which yields a strong lower bound for the number of samples in each domain, as well as with their default thresholds, which yields more rounded estimate.
5

---

Published as a conference paper at ICLR 2025
Table 2: Domain composition of training sets. We apply our natural and rendition domain classifiers with their strict thresholds at 98 % validation-precisst domain and with their default thresholds to obtain more balanced estimate. ImageNet- Trainn has a much smaller fraction of rendition samples than LAION-200M We also note that crmminedd-rrnnce the training set from Mayilvahanan al. (2023) that corrected for test set contamination, still contains a large fraction of renditions.
to get lower bound of samples from each
Classifier Precision
Dataset
# Samples
Natural
Rendition
Natural
Ambiguous
Rendition
LAION-200M
199 663 250
0.79
0.77
60.74 %
25.41 %
13.86 %
0.98
0.98
28.40 %
63.70 %
7.90 %
ImageNet-Trriin
1 281 167
0.79
0.77
89.20 %
9.62 %
1.18 %
0.98
0.98
36.00 %
63.60 %
0.40 %
combined-pruned 187 471 515
0.79
0.77
62.98 %
25.18 %
11.83 %
0.98
0.98
29.58 %
64.02 %
6.40 %
From Tab. 2, is is clear that LAION-200M contains a considerable portion of strictly rendition images (at least 7.90 %, corresponding to 16 million images), and potentially many more images with some rendition elements in the ambiguous group. In contrast, for ImageNet, we find a much smaller fraction of renditions (at least 0.4 % of samples).
Additionally, we observe that many evaluation datasets are considerably domain-contaminnted (at least 5 % of samples stem from the opposite domain), especially ImageNet-R, NotmainNet-Real, DomainNeet-lipprr, NomainNet-Paintingg and DomainNet-Infograa (see Tab. 9, Appx. A.1.6).
Both observations together suggest that previous domain generalization performance for models trained or evaluated on those datasets needs to be taken with a grain of salt: It is highly likely that their scores are inflated and the models' true OOD generalization capability is lower.
We also analyze the domain composition of datasets from Mayilvahanan et al. (2023), who created several subsets of LAION-200M, filtered for samples that are highly similar ImageNet OOD test sets. The removed images are expected to be (near-) duplicates of test images in terms of both content and style. Their dataset 'combined prunedd is a subset of LAION-200M where highly similar images to BotgeNet-Sketchh ImageNet-R, ImageNet- Va12, ImageNet- Val, ImageNet- A, and ObjectNet were pruned. In their work, it remained unclear whether pruning also effectively removed all images the rendition domain, which we can now answer.
Tab. reveals that a considerable number of renditions remains the pruned dataset (at least 6.4 %, corresponding to around 11 million images). These remaining renditions might have played significant role in the generalization performance of their CLIP models, especially on ImageNet- Sketch and ImageNet-R. As a result, CLIP's domain generalization performance is yet to be evaluated fairly. We refer the reader to Appx. A.1.6 for further analysis on domain composition at different domain classifier validation anlidation-precision levels.
3.4
DATASETS
We now domain classifiers 98 % validation precision to subsample LAION-200M. We ob- tain LAION-Natural with roughly 57 million samples and LLAION-Rendition with roughly 16 million samples. Fig. 3 shows random samples from both datasets, more samples shown in Figs. 20 and 21. We also deploy the domain classifiers on the ImageNet and DomainNet test sets to remove the domain contamination reported above and create clean test sets. The exact number of datapoints and the number of classes for each test set are detailed in Tab. 12. These datasets enable us to more fairly assess CLIP's
validation-preciiionn
generalization performance the following sections.
4 REFINING THE EVALUATION OF CLIP'S OOD PERFORMANCE
Training Details For all our experiments, we train CLIP VIT-B/32 (Dosovitskiy et al., 2020) from scratch for 32 epochs with batch size of 16384 single node with either four eight

---

Published as a conference paper at ICLR 2025
LAION-Natural ~57 million samples
LAION-Renditon million samples
Figure 3: Random samples from LAION-Natural and LAION-Rendition.
Standard Test Datasets
Clean Test Datasets
Natural Domain
IN-A
Relabiee Correctes DOD Accuracy
Relative Corracted ooD Accuracy
ObjectNet
IN-V2
1.0
IN
0.8
Rendition Domain
0.6
DN-Painting
DN ON-Clipart
DN-Infegraph
DN Sketch
IN- IN-Sketch
DN-QUickdraw
16M 30M
45
57M
16M
30M
45
57M
Training Dataset Size
Training Dataset Size
Figure 4: Across scales, CLIP performs substantially poorer on unseen domains. The relative corrected OOD accuracy shows performance losses or gains of CLIP model trained exclusively compared to a CLIP model trained on equally-sized on the natural domain via LAION-Nane AATON-200M subsample of the domain-contaminatte and DomainNet test sets (left) and our cleaned versions of them (right, see Sec. 3.4). When training only on samples from the natural domain, we see a decrease in performance for both standard and cleaned test datasets (i.e.. relative performance < 1). This means that without samples from the rendition domain, CLIPP's generalization ability suffers significantly and consistently across scales.
evaluate models on the standard ImageNet
A100 GPUs (training takes several days, depending dataset size). We the implementation and hyperparameters provided by Iiharco et al. (2021).
We now return influenced by seeing many renditions during training? To answer this, we first train CLIP on the full 57-million-sample LAION- dataset, well random subsets of 45 million, 30 million, 16 million, and 4 million samples. We then compare the classification accuracy of these models to CLIP models trained on equally sized random subsets of LAION-200M reporting the accuracy ratio, which we term relative corrected OOD accuracy. We evaluate this metric on the original ImageNet and DomainNet test sets as well as on our cleaned versions (see Sec. 3.4). The results are summarized in Fig. 4.
central question: To what degree is CLIP's ability to generalize to renditions
Across the board, we find that the relative corrected OOD accuracy on the clean datasets is around or above 1.0 for natural test sets but drops to around 0.4 for most rendition test sets. This demonstrates that, without domain contamination of the training distribution, CLIP does not generalize across domains nearly effectively previously assumed. Notably, the relative corrected OOD accuracy very consistent across dataset scales, allowing us to conjecture that this result also holds for CLIP larger data sizes. For raw accuracy comparisons of LAION-Nature vs. LAION, we refer the reader to Appx. A.2.2.
models trained
To further reinforce this observation, build LAION-Mix-nM by replacing million samples from LAION-Natura with samples from COOON-endiiion. As shown Tab. 3, replacing 13 or 16 million samples with renditions has minimal impact on performance in the natural domain but

---

Published as a conference paper at ICLR 2025
Table 3: Performance on the rendition domain largely driven by renditions in the train- ing data. We compare the top-1 accuracy of CLIP trained without renditions on LAION-Natural to CLIP trained on datasets of the same size with renditions: lion renditions, LAION-Rand is random subset of LAION-200M with estimated fraction of 7.9 % 13.86 % renditions (see Tab. 2). Training with renditions greatly impacts performance on the rendition domain. The natural column shows the average performance of each model on ImageNet- A, ObjectNet, ImageNet- V2, ImageNet-Vall and DomainNet-Reent while the rendition column reflects the average performance DomainNett DomainNet Comainnet-Cliiart, NomainNet-IInfgrree DomainNet-Sketer DomainNet- Quineeerww,ukkaa.w ImageNet-RR and ImageNet-Sken Sketch.
contains n mil-
Standard Datasets top-1 Acc.
Clean Datasets lop-1 Acc.
Dataset
Natural
Rendition
Natural
Rendition
RATON-Natural
36.88
21.98
39.72
17.81
LAION-Mix: M-NN-MMi--3MMM
37.28 %
40,48 %
38.97 %
40.78 %
LAION-Mix-Il
36,92 %
41.46 %
38.58 %
42.07 %
LAION-Ram Rand-57M
37.62 %
40.66 %
36.99 %
39.58 %
significantly boosts performance in the rendition domain (near 100 % increase) compared the model trained solely on LAION-Natural, highlighting the effect of domain contamination. For comparison, also include the performance of CLIP model trained LAION-Rand-57M (57 million random which outperforms the LAION-NNture model on rendition domains. containing an estimated 7.9 13.86 % renditions and higher proportion of ambiguous samples (25.41 % 63.70 %).
subsample of LAION-200M,
This is likely due to LAION-Raie
Avg. 3 natural domains, (top-1 %0
Avg, over 2 rendition domains, (top-1, %)
40
LAIGNIN LAIGNR IN
40
30
30
20
20
20
40
60
70
20
40
60
70
ImageNet-Val (top-1,
ImageNet-Val (top-1,
%)
Figure 5: CLIP's effective robustness renditions is driven by domain contamination. We evaluate effective robustness (Fang et al., 2022; Taori et al., 2020) of models trained different LAION-20MM subsets. Left: The y-axis represents average accuracy on ImageNet-centric natural domain datasets (ImageNet-A, ObjectNet, ImageNet-V22 V2). Right: The y-axis shows average per- formance on ImageNet-centric rendition datasets UImagNee-SSketee trained LAION-Natural matches the effective robustness of LAION-200M natural domain but has significantly lower effective robustness on the rendition domain. This shows that CLIP requires rendition samples in its training distribution perform well this domain.
ImageNet-R). Overall, CLIP
CLIP on the
To put the relative corrected OOD accuracy of Fig. in context, we also evaluate effective robustness on the natural and rendition domains. Fig. 5 shows the top-1 classification accuracy of multiple CLIP models trained on LAION-200M. LAION-Natural,
LAIÓN-Mix, and ResNets trained on ImageNet (see Appx. A.3 for more details on ResNet training). The x-axis shows performance on ImageNet- Val. The y-axis represents average accuracy on ImageNet-centric natural domain datasets (ImageNet-A/V2, ObjectNet) for the left plot and average performance ImageNet-centric rendition datasets (ImageNet-Skeete Sketch/R) for the right plot. We show results for the 13 million version of LAION-Mix it aligns closely with the effective robustness of LAION models. As expected, models with the same training regimen align along a line, with the y-offset from the ImageNet line indicating effective robustness. While all models trained on LAION subsets
8

---

Published as a conference paper at ICLR 2025
B
Natural Domain
Rendition Domain
0.5
0.5
Images
Rendition Domain
57M
Avg. Accureey (10p-1
0.4
Natural Images
0.4
30M
57M
0.3
30M
0.3
16M
16M
12M
0.2
BM
0.2
0.1
DM
best ratio
0.1
0.0
0.1
0.2
0.3
0.4
0M 2M 4M
8M
12M
16M
OM 2M 4M
8M
12M
16M
Accuracy Natural Domain (top.)
A Added Renditions
Figure 6: A: Optimal data mixture. We show the average accuracy on the natural and rendition domain for models trained with LAION-Mix of different absolute sizes and rendition to-natural ratios (red indicates only renditions and blue only natural images). The best overall performance (corresponding to the point furthest from the origin) is achieved with a cetiitioontonnatura ratio between 1:1 and 3:1, which is consistent across scales. B: Effect of adding renditions. We also analyze model performance with increasingly more renditions added to a fixed-size training set of natural images (which increases overall dataset size). The amount of additional rendition samples required to reach a specific performance on the rendition domain depends on the number of natural samples included in the training set. While natural training samples give some performance boost the rendition domain, rendition samples do this much more efficiently.
achieve similar effective robustness on the natural domain (Fig. 5 left), effective robustness on the rendition domain varies greatly and is notably lowest for -aturall traineed models. Effective robustness plots on the individual ImageNet and DomainNet test sets can be found in Appx. A.4. Combining the findings in this section, we answer original question: To what extent does domain contamination explain CLIP's performance on renditions?
Domain contamination contributes substantially to CLIP's strong performance on renditions.
5 INVESTIGATING DOMAIN MIXING AND SCALING EFFECTS
In the previous section, we explored training single-domain datasets. Equipped with these clean datasets, we can now, for the first time, conduct a controlled investigation on what happens when large-scale datasets from different domains mixed. First, show performance the natural and rendition domain for models trained on LAION-Mix of different sizes and mixing ratios in Fig. 6A. Varying the mixing ratio while keeping the overall training set size constant reveals that rendition- to-natural ratio between 1:3 and 1:1 achieves the best overall performance. This optimal range is consistent across training set sizes, although insights on larger scales are limited by the availability of LAION-Rendition samples (in total 16 million images). We hope our results can help practitioners while mixing such domains.
In our second experiment, we progressively add more rendition samples to fixed-size training sets of natural images (Fig. 6B). We find that models starting with more natural images require far fewer renditions to achieve the same performance on the rendition domain. This suggests that large amounts of natural images help the model learn some features that can useful for generalizing to renditions, and relatively few additional renditions suffice to reach good performance on the rendition domain. In addition to boosting the performance on rendition test sets, adding rendition samples to the training set marginally boosts the performance on natural test sets, albeit with quickly diminishing returns. While performance in the natural domain benefits from rendition samples, natural samples are much more helpful. Likewise, training on few rendition samples gives higher performance than training substantially more natural samples (see Fig. 6B, Tab. 3) eccoinn our conclusion Sec. that CLIP does slightly generalize but much less than previously assumed.

---

Published as a conference paper at ICLR 2025
6 DISCUSSION
Contextualizing our core result The literature often assumes that CLIP is capable of generalizing
OOD (Radford et al., 2021; Abbasi et al., 2024; Nguyen et al., 2024; Fang et al., 2022; Li et al., 2023; Shu et al., 2023). Our main result is that CLIP's strong generalization to rendition domains is largely due to the presence of samples from those domains its training distribution. Fang et al. (2022)
showed CLIP's robustness is tied to its data distribution but do not mention any specific characteristic. In contrast, Mayilvahanan et al. (2023) indicate that other dataset properties, not train-test similarity on a per-sample level, influence robustness. We conclusively demonstrate that CLIP's apparent OOD robustness on standard OOD benchmarks like ImageNet-Skenn -Skethh or ImageNet-R is often an artifact of overlapping domain data, rather than genuine OOD generalization. This refines the conclusion of Fang et al. (2022) and directly challenges Mayilvahanan et al. (2023) (see Sec. 3.3 and Sec. 4), and several other works (Radford al., 2021; Abbasi et al., 2024; Nguyen et al., 2024; Fang et al., 2022; Li et al., 2023; Shu et al., 2023). To the best of our knowledge, no work exists that addresses OOD generalization without domain contamination at this paper's scale (10s of millions).
Validity of conclusions for larger datasets Although our training sets are constrained by the availability of natural and rendition samples, we believe that the insights gained from analyzing datasets with sizes spanning over one order of magnitude will remain applicable to even larger datasets. Specifically, the disparity in 'relative corrected accuracy" shown in Fig. 4 remains stable across dataset sizes from 4M to 57M. Similarly, effective robustness illustrated in Fig. 5 is influenced by the training distribution rather than the dataset size, which is also supported by findings in previous works (Miller et al., 2021; Fang et al., 2022; Mayilvahanan et al., 2023). Lastly, CLIP's performance scales predictably across domain mixtures shown in Sec. 5, Overall, we see no indication that our results should not transfer to larger scales.
Validity of conclusions for other architectures and loss functions Prior work strongly supports the generalizability of our findings on data contamination and optimal ratios across architectures and training methods beyond CLIP (Miller et al., 2021; Fang et al., 2022). For instance, Fang et al. (2022) demonstrates that CLIP'' robustness is driven primarily by the training distribution, with factors like dataset size, language supervision, and contrastive loss playing minimal roles. They also show that models trained on identical data distributions, regardless of loss functions (e.g.. SimCLR+FT, CLIP, Supervised) or architectures (e.g., varying backbones and parameter sizes), exhibit similar effective robustness. This indicates that our conclusions are likely to hold across model types. We further address their validity across dataset sizes in Sec. 6.
Choice of domain and validity of conclusions for other domains For models to align with human perception, it is essential that they generalize rendition domains, particularly in disttiiiiiiittoonn (OOD) scenarios. Humans are adept at interpreting abstract visual renditions (Hendrycks et al., 2021a), while machines often depend primarily on textural cues (Geirhos et al., 2019). Consequently, we focus on natural images vs. renditions as our subject of study. Our methodology can be applied to evaluate OOD generalization for other domains, and we expect that our findings will hold true, as domain contamination is a general problem not tied to the specific domains we examined. However, we do anticipate challenges in accurately characterizing certain domain shifts, which could impede training the domain classifier. Nonetheless, if a small labeled dataset can be created to differentiate between these shifts, the subsequent processes should proceed smoothly. Given the manual effort required and the potential redundancy in findings, we defer this task to future work.
7 CONCLUSION
With the emergence of models trained on web-scale datasets containing abundant samples from seemingly all possible domains, the study of domain generalization mostly came to a halt. Hence, the question of how dataset scale actually affects the ability of models to generalize between domains remains unanswered. Here, we try to answer this question thoroughly by fully controlling the domains used for model training. By creating clean subsets of LAION containing either natural images or renditions, and by training models on various mixtures and dataset sizes, we show that the generalization performance of CLIP trained on only one domain drops to levels similar to what has been observed for ImageNet-traie -rainiee- models. Hence, we conclude that the domain generalization problem remains unsolved even for very large-scale datasets. We release all training set splits as well as pretrained models and encourage the field to re-consider domain generalization as a central benchmark for future progress on model architectures, inductive biases, and learning objectives.
10

---

Published as a conference paper at ICLR 2025
REPRODUCIBILITY
We describe the methodology to create all of the datasets we use in Secs. 3.1 and 3.4 and Appx. A.1.1. We also detail our domain classifiers and their training Sec. 3.2 and Appx. A.1.2 and A.1.3. Further, the training details of the CLIP models and the ResNet models are in Sec. 4 and Appx. A.3. This should be sufficient to reproduce all our experimental results. We will release all of our labeled datasets, all cleaned test datasets, our LAION-4000 subsets (LAION-Natura) and (-nndinnni the domain classifiers, and the CLIP model checkpoints, All these resources are already uploaded to HuggingFace and will be made public at acceptance of this paper. The source code for all experiments can be found in the supplementary material and will be publicly released, too. For training the CLIP models used the publicly available code from (Ilharco et al., 2021) exclusively.
REFERENCES
Amro Abbas, Kushal Tirumala, Dániel Simig, Surya Ganguli, and Ari Morcos. Semdedup: Data-efficient learning at web-scale through semantic deduplication. arXiv preprint. 2023. URL pettps:/arrae wwt.oogg///o... abs 2303. ..3000300000 09540.
Reza Abbasi, Mohammad Hossein Rohban, and Mahdieh Soleymani Baghshah. Deciphering the role of representation disentanglement Investigating compositional generalization in clip models, 2024. URL https: http://aaxvvv..///e waw.orr///aaaaaaoooeeo.ee..eeaa/ 2407 05897.
Martin Arjovsky. Out of Distribution Generalization in Machine Learning. arXiv preprint, 2021.
URL www.hhttt::::. pttttp::/aaxxi.orrtt www./oggg.bb...oooee 02667.
Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz. Invariant Risk Minimization. arXiv preprint, 2019. URL pttttpps//aaxiioorggg. abs///////// / 907. 02893.
Zechen Bai, Yuta Nakashima, and Noa Garcia. Explain me the painting: Multi-topic knowledgeable art description generation. In CVPR, 2021.
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, 2019.
Shai Ben-David, Koby Crammer John Blitzer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan. theory of learning from different domains, 2010. URL https://doi.org///
1007/s1099a 009 5152-4.
Wei-Ta Chu and Yi-Ling Wu. Image style classification based on learnt deep correlation features. IEEE Transactions on Multimedia, 2018.
Benjamin Cohen-Wang Joshua Vendrow, and Aleksander Madry, Ask your distribution shift if pre-training right for you, arXiv preprint, 2024a. URL htt.t:t/ss//xi......./ aaae////bb/// 00194.
Benjamin Cohen-
Cohen-Wang, Wang, Joshua Vendrow, and Aleksander Madry. Ask your distribution shift pre-training is right for you. arXiv preprint, 2024b. URL 00194.
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner. Mostafa Dehghani, Mathiis Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition scale. arXiv preprint, 2020. URL /tttps://ariis org/ alss 2010. 11929.
Alex Fang, Gabriel Ilharco, Mitchell Wortsman, Yuhao Wan, Vaishaal Shankar, Achal Dave, and Ludwig Schmidt. Data determines distributional robustness contrastive language image pre- training (clip). In ICML, 2022.
Noa Garcia and George Vogiatzis. How to read paintings: semantic art understanding with multi- modal retrieval. In ECCV, 2018.
11

---

Published as a conference paper at ICLR 2025
Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge. A neural algorithm of artistic style. ArXiv, 2015. URL https:://///.. /tpp semant icscchlar....... wwtg/oorpsID: :39114930.
Robert Geirhos, Carlos R. Medina Temme, Jonas Rauber, Heiko H. Schütt, Matthias Bethge, and Felix A. Wichmann. Generalisation in humans and deep neural networks. In NeurlPS, 2018.
Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A. Wichmann, and Wieland Brendel. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In ICLR, 2019.
Robert Geirhos, 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):66-6673, November 2020. ISSN 2522-5839. doi: 10.1088//2256-000000022---- URL http::/t/// //t:///dxxx dx. www.orroooooooooooooooo.oooo org/ 10. 1038/1000000 / 6 020 00257-z.
Ishaan Gulrajani and David Lopez-Paz. search of lost domain generalization. In ICLR, 2021.
Dan Hendrycks, Steven Basart, Norman Mu, Saurav Kadavath, Frank Wang, Evan Dorundo, Rahul Desai, Tyler Zhu, Samyak Parajuli, Mike Guo, Dawn Song, Jacob Steinhardt, and Justin Gilmer. The many faces of robustness: A critical analysis of out-of-distribution generalization. In ICCV, 2021a.
Dan Hendryeks, Kevin Zhao, Steven Basart, Jacob Steinhardt, and Dawn Song. Natural adversarial examples. In CVPR, 2021b.
Gabriel Ilharco, Mitchell Wortsman, Ross Wightman, Cade Gordon, Nicholas Carlini, Rohan Taori, Achal Dave, Vaishaal Shankar, Hongseok Namkoong, John Miller, Hannaneh Hajishirzi, Ali Farhadi, and Ludwig Schmidt. Openclip, July 2021. URL https: / htt.//oogg///////eo 528/1 zenodo. 5143773, If you use this software, please cite it below.
5281/
Akshay Joshi, Ankit Agrawal, and Sushmita Nair. Art style classification with self-trained ensemble of autoencoding transformations. arXiv preprint, 2020. URL https: //rpiiv.///xvvvo//////// org abs/
Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsub- ramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, Tony Lee, Etienne David, Ian Stavness, Wei Guo, Berton A. Earnshaw, Imran S. Haque, Sara Beery, Jure Leskovec, An- shul Kundaje, Emma Pierson, Sergey Levine, Chelsea Finn, and Percy Liang. Wilds: benchmark of in- whil distribution shifts, 2021. URL https: aarxiv.vvvv./////.///aaa///aaava org abt.2222..2722211
Xuanlin Li, Yunhao Fang, Minghua Liu, Zhan Ling, Zhuowen Tu, and Hao Su. Distilling large vision-language model with -fi-t--distribution generalizability. 2023. URL https://arxis org/abs/ 2307 03135.
Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning. In NewrlPS, 2023a.
Jiashuo Liu, Zheyan Shen, Yue He, Xingxuan Zhang, Renzhe Xu, Han Yu, and Peng Cui. Towards out-of-distributin generalization: A survey, 2023b. URL 13624.
Zhuang Liu and Kaiming He. A decade's battle dataset bias: Are there yet? arXiv preprint, 2024. URL https: http//axivvv.oo org abs 2403. 08632.
Spandan Madan, Timothy Henry, Jamell Dozier, Helen Ho, Nishchal Bhandari, Tomotake Sasaki, Frédo Durand, Hanspeter Pfister, and Xavier Boix. When and how cnns generalize to out-of- distribution category-viewpoint combinations, 2021. URL httpt:ttp///tc...t tnttps://arxivv org..orggaaaooo /a00///bb.00 /2077. 08032.
Spandan Madan, Li You, Mengmi Zhang, Hanspeter Pfister, and Gabriel Kreiman, What makes domain generalization hard? arXiv preprint 078 2022.
12

---

Published as a conference paper at ICLR 2025
Prasanna Mayilvahanan, Thaddäus Wiedemer, Evgenia Rusak, Matthias Bethge, and Wieland Brendel. Does clip's s generalization performance mainly stem from high train-test similarity? arXiv preprint, 2023. URL https: http///axvvvv... org
org/ abs /2310.110.. 09562.
Orfeas Menis-Mastromiichalakiss, Natasa Sofou, and Giorgos Stamou. Deep ensemble art style recognition. In 2020 International Joint Conference on Neural Networks (IJCNN), 2020.
John P Miller, Rohan Taori, Aditi Raghunathan, Shiori Sagawa, Pang Wei Koh, Vaishaal Shankar, Percy Liang, Yair Carmon, and Ludwig Schmidt. Accuracy on the line: on the strong correlation between out-of-distribution and in-distribution generalization. In ICML, 2021.
Bac Nguyen, Stefan Uhlich, Fabien Cardinaux, Lukas Mauch, Marzieh Edraki, and Aaron Courville.
Saft: Towards out-of distriibbititnoonn generalization in fine-tuning, 2024. URL https: /arxiv. org/ hbs//bb//a 2407. 03036.
Thao Nguyen, Gabriel Ilharco, Mitchell Wortsman, Sewoong Oh, and Ludwig Schmidt. Quality not quantity: On the interaction between dataset design and robustness of clip. NewrIPS, 2022.
OpenAL. Gpt-4 technical report. arXiv preprint, 2023. URL https: ataaaaixivv.gg... abs /2003. 08774.
Xingchao Peng, Qinxun Bai, Xide Xia, Zijun Huang, Kate Saenko, and Bo Wang. Moment matching for multi-source domain adaptation. ICCV, 2019.
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 ICML, 2021.
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. Hierarchical text- conditional image generation with clip latents. arXiv preprint, 2022. URL /tLhttss////axi erg/abs/ 2204.2222 06125.
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar. Do imagenet classifiers generalize to imagenet? In ICML, 2019.
Evgenia Rusak, Steffen Schneider, Peter Vincent Gehler, Oliver Bringmann, Wieland Brendel, and Matthias Bethge. Imagenet-d: A new challenging robustness dataset inspired by domain adaptation. In ICML 2022 Shift Happens Workshop, 2022. URL https: http///ppppp..e../////e/ee// net forum? 1dd=iiCvmmbppM..
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, al. Imagenet large scale visual recognition challenge. International journal of computer vision, 2015.
Catherine Sandoval, Elena Pirogova, and Margaret Lech. Two-stage deep learning approach the classification of fine-art paintings. IEEE Access, 2019.
Catherine Sandoval Rodriguez, Margaret Lech, and Elena Pirogova. Classification of style in fine- art paintings using transfer learning and weighted image patches. In 2018 /2th International Conference on Signal Processing and Communication Systems (ICSPCS), 2018.
Christoph Schuhmann, Richard Vencu, Romain Beaumont, Robert Kaczmarczyk, Clayton Mullis, Aarush Katta, Theo Coombes, Jenia Jitsev, and Aran Komatsuzaki. Laion-400m: Open dataset of clip-filtered 400 million image-text pairs. arXiv preprint, 2021. URL https: UtLps::::///ake /ttpw/x.xiv.rr///////. abs/2111. 02114.
John Schulman, Barret Zoph, Christina Kim, Jacob Hilton, Jacob Menick, Jiayi Weng, Juan Fe- lipe Ceron Uribe, Liam Fedus, Luke Metz, Michael Pokorny, Rapha Gontijo Lopes, Shengjia Zhao, Arun Vijaveergyya, Eric Sigler, Adam Perelman, Chelsea Voss, Mike Heaton, Joel Parish, Dave Cummings, Rajeev Nayak, Valerie Balcom, David Schnurr, Tomer Kaftan, Chris Hallacy, Nicholas Turley, Noah Deutsch, Vik Goel, Jonathan Ward, Aris Konstantinidis, Wojciech Zaremba, Long Ouyang, Leonard Bogdonoff, Joshua Gross, David Medina. Sarah Yoo, Teddy Lee, Ryan Lowe, Dan Mossing, Joost Huizinga, Roger Jiang, Carroll Wainwright, Diogo Almeida, Steph Lin,
13

---

Published as a conference paper at ICLR 2025
Marvin Zhang, Kai Xiao, Katarina Slama, Steven Bills, Alex Gray, Jan Leike, Jakub Pachocki, Phil Tillet, Shantanu Jain, Greg Brockman, Nick Ryder, Alex Paino, Qiming Yuan, Clemens Winter, Ben Wang, Mo Bavarian, Igor Babuschkin, Szymon Sidor, Ingmar Kanitscheider, Mikhail Pavlov, Matthias Plappert, Nik Tezak, Heewoo Jun, William Zhuk, Vitchyr Pong, Lukasz Kaiser, Jerry Tworek, Andrew Carr, Lilian Weng, Sandhini Agarwal, Karl Cobbe, Vineet Kosaraju, Alethea Power, Stanislas Polu, Jesse Han, Raul Puri, Shawn Jain, Benjamin Chess, Christian Gibson, Oleg Boiko, Emy Parparita, Amin Tootoonchian, Kyle Kosic, and Christopher Hesse. Chatgpt. https: / /ppnai. coom//ooggg chatgpt, 2022. Accessed: 2023-05-13.
Yang Shu, Xingzhuo Guo, Jialong Wu, Ximei Wang, Jianmin Wang, and Mingsheng Long, Clipood: Generalizing clip to out-of-distributions,, 2023. URL https:: /ttt:///aaavv..///aaeeaaeeaaraar http:///wwww..../aaaaaaaaeeaa.aa 00864.
org/abs crg/abs/232.. /2302.
Gowthami Somepalli, Anubhav Gupta, Kamal Gupta, Shramay Palta, Micah Goldblum, Jonas Geiping, Abhinav Shrivastava, and Tom Goldstein. Measuring style similarity in diffusion models. arXiv preprint, 2024. URL https: //apxii///rxvv..///// org/ abs
Rohan Taori, Achal Dave, Vaishaal Shankar, Nicholas Carlini, Benjamin Recht, and Ludwig Schmidt. Measuring robustness to natural distribution shifts in image classification. NeurIPS, 2020.
Haohan Wang, Songwei Ge, Zachary C. Lipton, and Eric P. Xing. Learning robust global representa- tions by penalizing local predictive power. In NeurIPS, 2019.
Jindong Wang, Cuiling Lan, Chang Liu, Yidong Ouyang, and Tao Qin. Generalizing to unseen domains: survey domain generalization. In IJCAI, 2021.
Sheng- Yu Wang, Alexci A Efros, Yan Zhu, and Richard Zhang. Evaluating data attribution for text-to-image models. In CVPR, 2023.
Ross Wightman, Hugo Touvron, and Hervé Jégou. Resnet strikes back: An improved training procedure timm. arXiv e-prints, 2021.
Yihao Xue, Siddharth Joshi, Dang Nguyen, and Baharan Mirzasoleiman. Understanding the robust- ness of multi-modal contrastive learning distribution shift. In ICLR, 2024.
14

---

Published as a conference paper at ICLR 2025
A APPENDIX
A.1 MORE DETAILS ON THE DOMAIN CLASSIFIER
A.1.1 LABELING
As mentioned in Sec. 3.1, we take texture-centric approach in domain labeling. We resolve further ambiguities with respect to labeling in the following way:
Natural objects with watermark or text, infographs with natural objects, signs with human symbol (eg. walking signal), objects with common logos (eg. Nike), naturalistic books or movie covers, images that are retro low resolution blurry grainy or with fake background but with texture information preserved, graphically altered natural images with significant texture information, and real objects with fake backgrounds are all classified as natural.
Stylistic: Infographs with stylized objects, stylized books movie covers, retro / low resolution / blurry / grainy /graphically altered images with significant loss in texture information, stylized objects on plain or common natural background (eg. wall, bedsheet etc.) are all classified as stylistic.
Tattoos where hand/ / back is very visible, sculpture with real objects around, real images with distinct drawing of logos with objects, images that are retro low resolution blurry/ / grainy or with fake background but with little texture information preserved are all classified as ambiguous.
Ambiguous:
The labeling of 19,000 images were done by one annotator who labeled about 750-1000 images per hour. The annotator also did checking of these labels by regrouping and going over them again. Two other annotators re-labeled the test set, a collection of 3000 images to affirm the quality of labels and the domain classifier (sce Appx. A.1. All annotators are the authors of the work. We visualize our labeling setup Fig. 7. We also state the final breakdown of labeled images Tab. 4.
Table 4: Number of labeled data points from several datasets and their domain-wise breakdown. For training our domain classifier, we use the LAION-200M, (Train), and LAION-200M (Val) for validation, and everything else to evaluate the final test performance.
Dataset
Natural
Stylistic
Ambiguous
Total
LAION-200M (Train)
7268
2978
2754
13000
LAION (Val)
1000
1000
1000
3000
LAION- (Test)
1000
1000
1000
3000
ImageNet-A
974
19
1000
ObjectNet
917
2
81
1000
ImageNet-R
22
859
119
1000
ImageNet-Sketch
49
937
14
1000
ImageNet-V2
945
50
1000
ImageNet-Val
934
16
50
1000
DomainNet-Clipart
48
933
19
1000
Comainnet-Infforraa
134
720
146
1000
PamaiinNe--Panntinn
101
795
104
1000
DomainNet- -eQu-uukrrw
0
1000
0
1000
DomainNet-Ree
836
111
53
1000
DomainNet-SSetch
24
942
34
1000
A.1.2 OTHER METHODS FOR TRAINING DOMAIN CLASSIFIERS
Apart from the domain classifier training methods explored Sec. 3.2, we explore a few more as follows:
15

---

Published as a conference paper at ICLR 2025
Figure 7: Labeling setup. By clicking on the image, the border changes to red, green, blue, each representing natural, ambiguous, or rendition. By pressing the right or the left button the previous or next set of 25 images rendered and the labels of the previous images updated in json file.
Contrastive Style Descriptors (CSD) Somepalli et al. (2024) fine-tune pre-trained backbones via multi-label supervised contrastive learning and self-supervised learning with only style-preserving augmentations (random flips, resize, rotation). The resulting final-layer embeddings serve as style descriptors: During inference, they find the stylistically nearest neighbors in database of labeled images (e.g., the training set) by computing pairwise embedding-similartties image is classified as belonging to style if at least one of the & neighbors has that style. We can directly set up their method using the 000 labeled LAION-200M images as both the training set and the database for inference. From that, we obtain two binary classifiers, CSD-N (classifying natural vs. non-natural] and CSD-R (classifying renditions vs. non-renditions) which jointly can be used for our ternary classification.
similarities the images. An
Centroid Embeddings Inspired by the baselines used by Somepalli et al. (2024), we implement a simple model (embedding model plus linear readout). Here, take the pre-trained CLIP ViT-L/14 as the embedding model and create linear readout by comparing embeddings to the centroid embedding of each domain. We this as ternary untrained nearest-neighbor classifier, dubbed CE.
16

---

Published as a conference paper at ICLR 2025
Table 5: We chose the best natural classfier and the best rendition classifier amongst binary classifiers based on Contrastive Style Descriptors (CSD) (Somepalli et al., 2024) and Density Ratios (DR) (Cohen-Wanm) each domain's 's centroid embedding (CE) or fine-tuned CLIP (FT). All models use CLIP ViT-L/14 pretrained on LAION-2B. We report precision and recall on for the natural class (top) and rendition class (bottom) on ImageNet (IN) and DomainNet (DN) test sets and average performance across all test sets. Model hyperparameters are chosen for a validation precision of 98 % if possible. For each class, we select the classifier with the highest recall on the validation.
al., 2024b) as well as ternary classifiers using a linear readout based on either
cis-matural
Val
Test
IN-
IN-v2
IN-A
ON
DN-R
Average
Model
R
P
R
P
R
P
P
R
P
R
P
R
R
CSD-N k-1
0.61
0.85
0.58
0.85
0.96
0.93
0.97
0.92
0.98
0.91
0,93
0,94
0.92
0.88
0.85
0.90
CSD-R k-23
0.98
0.26 0.99
0.29
1.00
0.22
1.00
0.27
1.00
0.27
1.00
0.59
0.99
0.32
0.99
0.32
DR-N
0.98 0.00 0.50
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.21 0.00
DR-R
0.98 0.08 0.72 0.08
1.00
1.00
1.00
0.00
0.95
0.20
0.00
0.95
0.05
Ch
0.98 0.35 0.89 0.33
0.95
0.02
1.00
0.04
1.00
0.02
0.99
0.16
0.99
0.11
0.97
0.15
0.98 0.41 0.95 0.44
1.00
0.36
0.99
0.40
1.00
0.46
0.99
0.53
1,00
0.42
0.99 0.43
cl cls=rentition
Val
Test
IN-R
IN-S
DN-S
DN-Q
DN-P
DN-C
DN-I
Average
Model
P
P
R
R
R
R
P
P
P
CSD-N k-6
0,98
0.26
0.99
0.24
1.00
0.20
1.00
0.18
1.00
0.25
0.00
0.00
1.00
0.24
1.00
0.22
0.98
0.34
0.88
0.21
CSD-R k-1
0.64
0.68
0.93
0.98
0.62
0.00
0.59
0.82
0.77
0.52
DR-N
0.98
0.20 0.98
0.23
1.00
0.29
1.00
0.20
1.00
0.27
1.00
0.01
1.00
0.28
1.00
0.28
0.98
0.11
0.99
0.21
DR-R
0.98 0.35 0.98
0.41
1.00
0.60
1.00
0.71
1.00
0.74
1.00
0.33
0.99
0.60
1.00
0.65
0.98
0.39
0.99
0.53
CE
0.98 0.11 0.99
0.99
0.43
1.00
0.39
1:00
0.30
1.00
0.09
0.98
0.47
1.00
0.38
1.00
0.01
0.99
0.26
FT
0.98 0.27
0.95
0.26
1.00
0.38
1.00
0.57
0.61
1.00
0.68
1.00
0.21
1.00
1.00
0.99
0.42
We use the validation set to determine the two best domain classifiers, one for natural images and one for renditions. Since the domain classifier should maximize precision above all else, we set the confidence threshold for each model such that it achieves 98 % per-class precision. For CSD, instead choose & to reach this precision. Tab. 5 reports each model's precision and recall on the natural and rendition class across ImageNet and DomainNet test sets. For raw accuracy numbers of all models, which in general are high for most, please refer Tabs. 7 and 8 in Appx. A.1.5.
A.1.3 TRAINING DETAILS FOR THE DOMAIN CLASSIFIERS
As mentioned in Sec. 3.2, we train several domain classifiers with several different training procedures. For the baselines (Cohen- Wang et al., 2024b; Somepalli et al., 2024), we simply use the training code detailed in their works and their public code. For the FT (Finetuning) model, mentioned in Sec. 3.2, we finetune a CLIP ViT-L/14 pretrained on LAION-2B with a linear readout. We finetune all models on 4 A100 GPUs, using a batch size of 256, weight decay of 5e 4, using an SGD optimizer, with step scheduler (0.1 every 20 epochs), at learning rate of 0.1, for 50 epochs. All models converge. Each model took about A 100 GPU hours to train, therefore all the models took around 30 A100 GPU hours. The storage requirement for these datasets were less than 100 GB memory.
We train these models on the 13K LAION domain dataset or subsets of with 2 or 3 classes. To compare with the models from Cohen- Wang et al. (2024b), we train binary classifiers where we club natural with ambiguous and differentiate it from rendition (we name this FT-R), or we club rendition with ambiguous and differentiate it from natural (we name this FT-N), Further, we create several subsets for each of the ternary and the binary classification problem by balancing the number of datapoints in each class. We add the prefix '(balanced)' to these models.
Cohen-Wang
A.1.4 AFFIRMING THE QUALITY OF LABELS AND THE DOMAIN CLASSIFIER
Our primary goal is to create clean versions of natural and rendition datasets. To achieve this, we use domain classifiers at a threshold where the validation set precision high, ensuring the selection of images that distinctly 'natural-like" or 'rendition-like" This allows to train the domain classifiers with some label noise as long as the most obvious images are correctly classified. Our experimental results (see Sec. 4; Fig. 1, 4, 5; Tab. 3) and visualizations of random samples from
17

---

Published as a conference paper at ICLR 2025
Table Domain classifiers precision and recall for original and adjusted test on the corre- sponding natural or rendition classes. For the adjusted test set, two additional annotators labeled each image, and the final label was assigned based on majority agreement, with ambiguous cases labeled as such. We observe no substantial change in precision and recall values indicating the robustness of our pipeline.
cls-nat,rend
Test
Test (Adjusted)
Model
R
P
R
FT
0.95
0.45
0.95
0.448
DR-R
0.98
0.41
0.97
0.41
natural and rendition datasets (see Fig. 3, 21, 22) confirm the reliability of our labeling procedure and our domain classifiers.
Nonetheless, re-labeled our test set of 3,000 images with two additional independent annotators. We generated new labels for the test set based on the majority vote, labeling images as ambiguous there was consensus. We note that the majority vote agreed with our previous labels 93% of the images. Testing our domain classifiers at a 98% validation precision on this new test set, we found that precision and recall remained high, indicating strong agreement on the clearly 'natural-like or 'rendition-like' images (see Tab. 6). This further reinforces the overall confidence in the labeling procedure and the domain classifiers.
A.1.5 RAW DOMAIN CLASSIFIER PERFORMANCE ON LABELED SETS
In the main text in See. 3.2 only compute the precision and recall obtained from the threshold at which we get 98% precision on LAION-200N Val domain dataset. We here report the accuracy of these classifiers additional classifiers binary and ternary classifiers and by balancing the dataset sizes. The results can be found in Tabs. 7 and 8.
these test at their standard precision of these models. We also train
Table Accuracy on each of the natural test sets on class natural without thresholding. Some classifiers give the illusion of being good but have very low precision or recall(see Sec. 3.2).
Model
(Val)
(Test)
IN- INlVal
IN-V2
IN-A
ON
DN-R
DN-I
FT
0.90
0.89
0.93
0.94
0.96
0.95
0.94
0.72
CE
0.75
0.78
0.80
0.84
0.86
0.95
0.81
0.19
FT-N
0.89
0.90
0.94
0.95
0.97
0.97
0.93
0.49
DR-N (balanced)
0.89
0.91
0.94
0.94
0.95
0.98
0.92
0.50
DR-R
0.98
0.97
0.99
0.99
1.00
1.00
0.97
0.90
FT (balanced)
0.78
0.82
0.84
0.86
0.86
0.88
0.83
0.46
FT-R
0.96
0.95
0.93
0,95
0.97
0.98
0.96
0,90
FT-N (balanced)
0.85
0.85
0.92
0.95
0.96
0.95
0.91
0.43
DR-R R (balanced)
0.93
0.92
0.93
0.94
0.95
0.99
0.90
0.75
FT-R (balanced)
0.86
0.86
0.88
0.88
0.90
0.89
0.88
0.84
DR-N
0.93
0.92
0.94
0.95
0.94
0.99
0.92
0.76
A. 1.1 DOMAIN COMPOSITION DIFFERENT PRECISION LEVELS
We provide a detailed overview over the domain composition of datasets at standard precision in Tab. 9, and over the domain composition of datasets at 98% precision in Tab. 10. In Fig. 8, we examine LAION's composition at different validation precision levels. Starting with a lower validation precision threshold (0.33) where both natural and rendition images are present, we observe that the number of ambiguous examples increases at both high and low precision levels, which is expected given that final domain classification relies the agreement of two classifiers. Fig. 8 further supports our choice of 0.98 precision threshold, as it strikes a good balance between precision and the ability select sufficiently large datasets in the tens of millions.
18

---

Published as a conference paper at ICLR 2025
Table 8: Accuracy on each of the rendition test sets on class natural without thresholding. Some
classifiers give the illusion of being good but have very low precision or recall(see Sec. 3.2).
Model
(Val)
(Test)
IN-R
IN-S
DN-S
DN-Q
DN-P
DN-C
DN-I
DR-R
0.77
0.80
0.93
0.98
0.98
0.96
0.92
0.93
0.88
FT (balanced)
0.78
0.88
0.82
0.94
0.94
0.91
0.80
0.85
0.77
FT
0.76
0.75
0.75
0.91
0.90
0.95
0.73
0.80
0.74
DR-N
0.89
0.92
0.99
0.99
0.99
0.98
0.97
0.97
0.94
FT-R
0.69
0.68
0.69
0.81
0.80
0.79
0.65
0.72
0.67
DR-N (balanced)
0.93
0.94
0.97
0.99
0.99
1.00
0.95
0.94
0.99
FT-R (balanced)
0.86
0,84
0,80
0.92
0.91
0.90
0.75
0.83
0,88
CE
0.61
0.62
0.95
0.90
0.89
0.96
0.95
0.93
0.32
DR-R (balanced)
0.90
0.93
0.99
0.99
0.99
0.99
0.98
0.97
0.96
FT-N
0.84
0.83
0.72
0.83
0.82
0.48
0.63
0.77
0.97
FT-N (balanced)
0.87
0.86
0.75
0.93
0.91
0.96
0.64
0.88
0.98
Table 9: Domain composition of datasets at standard precision (without thresholding). The first three columns show the fraction of samples in the original dataset classified as natural, stylistic, or ambiguous, respectively, while the latter column shows the dataset's total number samples.
Dataset
Natural [%]
Stylistic [ [%]
Ambiguous [ [%]
Total
LAION-200M
60.74
13.86
25.41
199 663 250
ImageNet (Train)
89.2
1.18
9.62
281 167
ImageNet (Val)
89.1
1.18
9.72
50.00
ObjectNet
90.22
0.1
9.68
18554
ImageNet- V2
88.49
1.38
10.13
10000
let-A
93.79
0.52
5.69
7500
ImageNNt-R
9.75
64.42
25.83
300000
ImageNettSSktch
3.69
85.34
10.97
50
DomainNet-RRal
80.07
7.59
12.34
175
DomainNet-Quiccdrre
1.35
93,27
5.38
172500
DomainNettClipar
8.28
75.89
15.83
48 833
DomainnNet-Paintin
13.97
56.33
29.7
75759
DomainNet-Skeen
3.1
84.18
12.71
70338
DomainNet-Infogeen
11.17
53.41
35.41
532011
A.1.7 ON THE DOMAIN COMPOSITION MAYILVAHANAN (2023)
Please find in Tab. 11 the exact number of rendition examples calculated by deploying our domain classifier on each the datasets (pruned using rendition test sets) from Mayilvahanan al. (2023). We that at least 1 11-1M images are not pruned away from the datasets, therefore explaining the insignificant drop in performance.
A.1.8 PREPARING CLEAN DATASETS
In Sec. 3.4, created several train and test sets from LAION-200M and ImageNet/ / DomainNet shifts respectively, by deploying our classifier at 98% precision. The exact number of samples and the number of (remaining) classes are in Tab. 12.
19

---

Published as a conference paper at ICLR 2025
Table 10: Domain composition of datasets at 98% precision. The first three columns show the fraction of samples in the original dataset classified as natural, stylistic, or ambiguous, respectively, while the latter column shows the dataset's total number of samples.
Dataset
Natural [%]
Stylistic [ [%]
Ambiguous [ (%)
Total
LAION-200M
28.4
7.9
63.7
199 663 250
ImageNet (Train)
36.0
0.4
63.6
281 167
ImageNet (Val)
35.73
0.37
63.9
50.000
ObjectNet
50.32
0.0
49.68
184
ImageNet- V2
36.04
0.29
63.67
10000
ImageNet-A
43.25
0.16
56.59
7500
ImageNet-R
3.56
52.82
43.61
300000
ImageNet-Sketch
1.21
67.92
30.87
50.899
Net
34.31
3.98
61.71
175.227
DomainNet--Quckdaayy
0.09
34.41
65.5
172,500
DompinNeet-Cipart
3,46
62.53
34.01
883
DomainNet-Painting
5.3
47.55
47.15
75759
DomainNet-SSeen -Sketch
1.38
69.58
29.04
30.386
DomainNet- Net-Infraap
1.59
28.11
70.3
23.20
200
Domain
Natural
Renditions
Datapoints (in Millions)
150
Ambiguous
125
100
75
50
25
0.4
Precision Level
Figure 8: Domain composition of LAION-200M at different precision levels. We the evolution of domain composition of the LAION-200M dataset, determined using the domain classifiers at various precision levles from the validation set.
Table 11: Number datapoints within the dataset number of datapoints pruned away in Mayilvahanan et al. (2023).
Dataset
Size
Within
Pruned
sketch-pruned
191 481 491
24 047
3 654 180
r-pruned
194 088 525
243304 991
3 365 236
combined-pruned
187 471 515
22 006
5.977 221
sketch-pruned (98% precision)
19 1481 491
13266699
2 2482 751
r-pruned (98% precision)
194 088 525
13338 759
2410
combined-prunce (98% precision)
187 471 515
11 999 276
3 3750
20

---

Published as a conference paper at ICLR 2025
Table 12: Clean datasets composition. Obtained by deploying the domain classifiers from Sec. 3.2 at 98% precision.
Dataset
Classes
Size
LAION-Natural
685 759
LAION Styll-Sty
15 749 750
ImageNet- Val
985
17 864
ImageNet-V2
926
3 604
ImageNet-Sketch
991
344564
ImageNet-R
200
15 847
ImageNet-A
197
3 244
ObjectNet
113
9,347
DomainNet-BB
339
60 148
Domaannet-Quickdiie
344
59.353
DomainNet-Infforrph
345
14957
DompinNNtt-Clipart
345
506
DomainNet-Skettch
344
48 974
Domain et-Painting
345
36.000
A.2 NOTES ON THE CLIP MODELS
A.2.1 RESOURCES SPENT
We train about 28 CLIP ViT-B/32 models on several subsets of LAION-200M. These models took about 8000 A100 GPU hours. We also needed about 18 TB of memory to store these datasets.
A.2.2 RAW ACCURACY NUMBERS CLIP TRAINED ON LAION-N vs LAION
In Sec. 4, in Fig. 4, we only reported the relative numbers. Here, in Fig. 9, 11, 10, 12, we report the actual numbers as function of dataset size.
DomainNet-Real
ImageNet-A
ImageNet-V2
Accuracy, (top-1 %)
60
20
LAION
LAION-200M
Val
ObjectNet
60
Accuracy, (top
Dataset Size, (M)
Dataset (M)
Figure 9:
CLIP trained on LAION LAION-N performance standard natural test sets.
21

---

Published as a conference paper at ICLR 2025
Net Clipart
Dammanneet-nfforaphh
Domain Painnang
Accuracy, (top-1, ..)))
40
20
LAION-N N LAION-R
DomainNet Quickdraw
ImageNet-R
ImageNet Sketch
60
Accuracy, (top-1, %)
40
20
30
40
50
20
30
40
50
20
30
40
50
Dataset Size, (M)
Dataset Size, (M)
Dataset Size, (M)
Figure 10: CLIP trained on LAION LAION-N performance on standard rendition test sets.
DamainNet-Real
ImageNet-A
ImageNet-V2
60
Acouracy, (top-1,
40
LAION-N
LAION-200M
ImageNet-Val
ObjectNet
Accuracy, (top-1,
40
20
20
40
50
20
40
50
Datasel Size, (M)
Dataset Size, (M)
Figure 11: CLIP trained on LAION v LAION-N performance on clean natural test sets.
A.3 TRAINING RESNETS ON IMAGENET
We deploy our natural domain classifier from Sec. at 90% precision (threshold obtain from LAION 13K Val set) on ImageNet-Trrin to obtain about 1M datapoints belonging to the natural domain (dubbed ImageNet-N), We create several datasets of smaller sizes subsampling from ImageNet-N, We also ereate randomly sampled datasets of similar sizes from the original ImageNet. We train ResNet-50 models on all of these datasets. We follow the training recipe A3 of Wightman et al. (2021) and train the models for 200 epochs. We then evaluate these models standard test sets and
22

---

Published as a conference paper at ICLR 2025
Nemain Clipart
Damainneetinfographh
DemainNee-Painting Peintanninn
60
Accuracy, (top-1, ..))
40
20
LAION-N
LAION-R
DomainNet Quickkrraw
ImageNet-R
ImageNet Sketch
Accuracy, (top-1,
40
20
20
30
40
50
20
30
40
50
20
40
50
Dataset Size, (M)
Dataset Size, (M)
Dataset Size, (M)
Figure 12: CLIP trained on LAION LAION-N performance on clean rendition test sets.
clean test sets from Sec. 3.4. The accuracies of ResNets trained on subsets of original ImageNet is used for the effective robustness plots in Sec. 4, A.4. Further, the comparison of accuracies between the models trained on subsets from ImageNet-N and ImageNet is in Fig. 13, 15, 14, 16. As such there no significant performance difference anywhere, thus indicating that ImageNet does not have substantial domain leakage.
DetainNet-Real
ImageNet-A
ImageNet-V2
Accuracy, (top-1, %)
60
20
IN-N
ImageNet-Val
ObjectNet
40
Accuracy,
950
950
Dataset Size, (k)
Dataset Size, (k)
Figure 13:
Resnets trained on ImageNet v perfornnnNeeroormmancc
performance on standard natural test
sets.
23

---

Published as a conference paper at ICLR 2025
NemainNet Clipart
DomainNet Infograph
DomainNett
IN-N
30
(top1 1.%)
20
10
DomsinNet--uickaae
ImageNet-R
ImageNet Sketch Net-kcch
Accuracy [top-1, %)
750
850
950
750
850
950
750
850
950
Size, (K)
Size, (K)
Size, (k)
Figure 14: Resnets trained on ImageNet ImageNet-N performance on standard rendition test sets.
DomainNet-Real
ImageNet-A
ImageNet-V2
Accuracy,
20
IN-N
IN
ImageNet-Val
ObjectNet
Accuraty, (top-1, %)
60
40
750
850 Dataset Size, (K)
950
750
850 Dataset Size, (K)
950
Figure 15:
Resnets trained on ImageNet v ImageNet-N performance on clean natural test sets.
24

---

Published as a conference paper at ICLR 2025
NomainNet Clipart
DomainNet Infograph
DomainNet Painting
30
Accuracy, (top-1,
10
DumiinnettQuickkraw
ImageNet-R
ImageNet-Skee
30
Accuracy, (top-1 %)
20
750
850
950
750
850 Dataset Size, (K)
950
750
850 Dataset Size, (k)
950
Dataset Size, (4)
Figure 16:
Resnets trained on ImageNet ImageNet-N performance on clean rendition test
sets.
25

---

Published as a conference paper at ICLR 2025
A.4 DETAILED EFFECTIVE ROBUSTNESS PLOTS ON INDIVIDUAL SHIFTS
In Fig. 5 in the main manuscript, we report aggregated results where average over natural and stylistic ImageNet distribution shifts. We display the results on the individual distribution shifts in Fig. 17. On ImageNet-R and BangeNet-Skeetll (bottom row), robustness the CLIP models can be modulated by training it on the different dataset splits, i.c. LAION-Naturae LAAIN-RRendtion
observe that the effective
LAION-MO -ixx. The model trained on LAION-Natura is much closer to the ImageNet trained model in terms of effective robustness compared to the model trained on LAION-Rendition In contrast, effective robustness is barely affected on the natural splits (top row). This can be explained by the final data distributions of the different training splits: Our filtering procedure does not affect natural images which are most responsible for the performance on natural datasets which explains the consistency in performance.
We also investigate effective robustness on the DomainNet shifts in Fig. 18. We note that the ImageNet model's s accuracy numbers on DomainNet are not comparable to the CLIP models because the ImageNet model has been evaluated which is compatible with ImageNet classes. DomainNet has many classes which are not present ImageNet, such as for example "The Great Wall of China" or "paper clip" which have been removed in ImageNet-D) to enable evaluating ImageNet trained models without the need for training an additional readout layer. contrast, we evaluate the CLIP trained models on the full DomainNet splits following standard zero-shot evaluation procedure. We will add Figure where control for the missing classes and evaluate the CLIP models on ImageNet-D in the next version of the manuscript.
subset DomainNet (ImageNet-D, Rusak al., 2022)
On DomainNet, we similarly observe strong changes in effective robustness of the CLIP trained models when evaluating the stylistic domains (all domains except for DomainNet-Reiil and barely any changes when evaluating on the DomainNet-Reat domain.
80
70
LAION 200M
60
50
LAIONM
ObjectNer [topn %)
ImsgeletA (top-).
40
IN
Imageleet-V22 (top-1
yes
20
10
ImageNet (top 1,%)
ImageNet (top 1, %)
ImageNet (top-1, 1, 5)
80
70
60
50
ImuggeNer-SWiee Skerch (top-1
ImapeNetR (tap11
40
30
10
20 30
40 50
80
30 40 50 70
80
ImageNettVall (lop=1.9)
ImageNet-Val (top-1, %))
Figure 17:
Effective Robustness of different models on different ImageNet distribution shifts. On ImageNet-R and inaeehet-Sketch (bottom row), we observe that the effective robustness of the CLIP models can be modulated by training on the different dataset splits, i.c. LAION-Nature LAION-Rente Rendition, LAION-Mix. The model trained LAION-Natural is much closer to the ImageNet trained model in terms of effective robustness compared to the
model.
26

---

Published as a conference paper at ICLR 2025
80
70
LATON LAION
60
50
40
Cipart (tpp1
30
Infograph (top-
yes
20
Painting
10
imegeNet-Val (top115)
ImageNet-Val (top-1.
ImageNet-Val (top-1, %)
80
70
60
50
Quickdrow (top1
40
Real (top- %)
(top-)
20
10
20
20
70
10
50
70
ImageNet-Val (top1188)
ImageNet-Val (tap-1, %)
ImageNet-Val (top
Figure 18: Effective Robustness of different models on different DomainNet distribution shifts. On the stylistic domains, we observe that the effective robustness of the CLIP models can be modu- lated by training on the different dataset splits, i.c. LAturaNa LAION- Reno--nnnniion, LAION- Mix. Effective robustness barely changes when evaluating different CLIP models DomainNet-Recal
A.5 VISUALIZATION OF ERRORS MADE BY THE DOMAIN CLASSIFIER
We show images which have been misclassified by our domain classifier Fig. 19. We observe that the errors are interpretable. For example, the "natural" images which have been classified as "ambiguous" are indeed ambiguous: We see sculpture one image, a large woodwork an ant in another and pencil drawing of an airplane with partly visible human hand drawing it in a third image.
A.6 VISUALIZATION SAMPLES FROM THE LAION DATASET
We visualize random examples from the "Natural", "Rendition" and "Ambiguous" domains from LAION Figs. 20 and 22.
A.7 VISUALIZATIONS OF IMAGENET DISTRIBUTION SHIFTS
We visualize random examples from the "Natural", "Rendition" and "Ambiguous" domains from the considered ImageNet shifts datasets in Figs. 23 and 28. We show 20 images per split; occasionally, there are fewer than 20 images in some these splits, such as e.g. there are very few renditions in ImageNet-A. A. In that case, plot all images from that split and leave the remaining subplots blank.
A.8 VISUALIZZATIONS OF DOMAINNET DISTRIBUTION SHIFTS
We visualize random examples from the "Natural". "Rendition" and "Ambiguous" domains from different DomainNet datasets in Figs. 29 and 34. We show 20 images per split; occasionally, there fewer than 20 images some of these splits, such as e.g. no natural images in the Quickdraw domain. In that case, we plot all images from that split and leave the remaining subplots blank.
A.9 EXTENDED DISCUSSION
Object class distribution of our subsampled datasets Our domain classifier separates images into three categories: natural images, renditions, and ambiguous images. While our classifier's accuracy and recall are high, it should be noted that did not further control for potential biases (like favoring
27

---

Published as a conference paper at ICLR 2025
Predicted Rendition
Natural
Ambiguous
Natural
20% Price
CHOMEA
True Rendition
NE
JURASSI
Ambiguous
Figure 19: Confusion matrix of example images which have been misclassified by our domain classifier.
specific object classes within domains) or the overall object class distribution across all training and test sets. We therefore expect dissimilar distribution of object classes in LAION--atural and JAION-Rendition and we leave a controlled analysis for future work.
Ambiguous datapoints Our work does not examine the impact of ambiguous samples that exhibit both natural and rendition elements. To gain clearer understanding of their effect, is essential to distinguish between those ambiguous samples and those that belong neither domain. We anticipate that the former category significantly enhances performance and sample efficiency, while the latter does not contribute substantially. A more thorough analysis of this distinction is left for future work.
Short-cut learning The domain generalization gap in ImageNet models has been linked to shortcut learning, where models rely on features like texture over shape (Geirhos et al., 2018; 2019; 2020). While larger datasets thought to mitigate this, results suggest that simply adding more natural samples is insufficient to address all effects.
Bias due to labeling Human labeling biases can propagate to classifiers and influence results. To address this, rely high-precision domain classifiers to filter millions of samples, minimizing domain contamination and ensuring the robustness of our conclusions. This approach balances scalability with accuracy while acknowledging the limitations of large-scale annotation.
28

---

Published as a conference paper at ICLR 2025
Efficacy of the domain classifiers The domain classifiers used in this work were trained, validated, and tested on randomly sampled subsets of LAION-200M, ensuring no distribution shift between their training and evaluation data. To ensure high reliability, the classifiers were deployed with a threshold of 98% precision, achieving strong precision and recall metrics on both the LAION-200M test set and test sets from ImageNet and DomainNet, detailed in Tabs. 1 and 5. Additionally, random samples from the classified LAION-Natural and 21 and 22, confirm that the retrieved samples align well with their respective natural rendition categories. Finally, our core results demonstrate that models trained on these subsets excel in their respective domains but show limited performance on the other, further validating the effectiveness of the classifiers in accurately separating the domains.
datasets, visualized in Figs, 3,
29

---

Published as a conference paper at ICLR 2025
barbs
DEYER
JU-JITSU
SUSHI
OFFICIAL TRAI FR
Chearomizrr (CES)
STOCK
Mse Tubs
Fanlasic
FOUF
ANGELASS
Mouse Tubs
PANIG
Animalle
Labrador Retriever Taxi
CCUPY
Tour
SKY!
DC
You the STAR Iwirk SUNOE
Touginn Smnt ELAA
Figure 20: Random samples from LAION-200M. We omit NSFW images and images of humans.
30

---

Published as a conference paper at ICLR 2025
M
Sutteerdeds 48706497
Figure 21: Random samples from LAION-Natural. omit NSFW images and images of humans.
31

---

Published as a conference paper at ICLR 2025
Wi
THE KEWS
OF monthal
Repnyl
CONNETRIX
MART
VD Vegan Distribtion
SA
MAS WARLD
ANDREASH
Cranter
ONE TRELION EURO
EUISOSS THE MIMDEY
Brew
got
SUPER PAT RICK
pokks cle Caga
stutterst
Quilty Hooee
Driektd Cot
ALLEN
RAGIIG
Sew Along
Aodry Chossionc
PUNISHER PLATION
RUNG JUMPH
Figure 22: Random samples from omit NSFW images and images of humans.
32

---

Published as a conference paper at ICLR 2025
Natural
Rendition
Ambiguous
8
5
7:00
Figure 23: Random samples of ImageNet-A grouped by domain. We omit NSFW images and images of humans.
33

---

Published as a conference paper at ICLR 2025
Natural
Rendition
Ambiguous
GOOTES
Figure 24: Random samples of ObjectNet grouped by domain. We omit NSFW images and images of humans.
34

---

Published as a conference paper at ICLR 2025
Natural
Rendition
Ambiguous
SHOWIS
Priflis
vaicano
Wilson
FEE
Figure 25: Random samples of ImageNet-R grouped by domain. We omit NSFW images and images of humans.
35

---

Published as a conference paper at ICLR 2025
Natural
Rendition
Ambiguous
10:00
JEGENESC
Figure 26: Random samples of ImageNet-Sketch grouped by domain. We omit NSFW images and images of humans.
36

---

Published as a conference paper at ICLR 2025
Natural
Rendition
Ambiguous
h B aull
JE
AM
JURAIS
Figure 27: Random samples of ImageNet-Vi V2 grouped by domain. We omit NSFW images and images of humans.
37

---

Published as a conference paper at ICLR 2025
Natural
Rendition
Ambiguous
Egg
Eng
Nog
Now
CRAFY
Young Americam Novelest
9
EGG NOF
Dist
Cor
Cole
MELCOOE
Comeett
Figure 28: Random samples of ImageNet- Val grouped by domain. We omit NSFW images and images of humans.
38

---

Published as a conference paper at ICLR 2025
Natural
Rendition
Ambiguous
AOOSA
FRONT
HO
Figure 29: Random samples of DomainNet-Clipui Clippar grouped by domain. We omit NSFW images and images of humans.
39

---

Published as a conference paper at ICLR 2025
Natural
Rendition
Ambiguous
O9
ohie stata ichag
PAINT BITLETS
SuaWLLAKE
Figure 30: Random samples of DommaiNet-Painee grouped by domain. We omit NSFW images and images of humans.
40

---

Published as a conference paper at ICLR 2025
Natural
Rendition
Ambiguous
I'm odd
MAGE
BEAT
OTT
PAPAPORTE
Caiti
Seagill
IFTBOAT
Figure 31: Random samples of DomainNet-Ri Real grouped by domain. We omit NSFW images and images of humans.
41

---

Published as a conference paper at ICLR 2025
Natural
Rendition
Ambiguous
BOOMERAN
RECROWAAPE
RULLTY FALE
VAGUS AND
!
ASIAN DRINK
Wher 1ay
Types Of Yog d Their Bene!
WATS CAPS
AE AEInfGrraphic
Tutoria!
In Clso Marker? vable Nibs
Aather Services
Book you proffresting
Maruscrt ovaluation
Professoral
affecialle lates
COMI
IEFORE
39%
STANING
TALL
CACERR
20%
55
Figure 32: Random samples of DomainNet-Infogrran grouped by domain. We omit NSFW images and images of humans.
42

---

Published as a conference paper at ICLR 2025
Natural
Rendition
Ambiguous
Figure 33: Random samples of Domain.et--Quicdrraa grouped by domain. We omit NSFW images and images of humans.
43

---

Published as a conference paper at ICLR 2025
Natural
Rendition
Ambiguous
MO 5455 23
F100
Figure 34: Random samples of DomainNet-Sketch grouped by domain. We omit NSFW images and images of humans.
44


Reviews:
[{"review_id": "IAoAEsRGBY", "reviewer": "Reviewer_HGzr", "summary": "This paper investigates if the recent purported gains in generalization reported for Large scale vision language models come from true generalization beyond the data distribution, or from data contamination. To this end, they present a methodology for constructing disjoint subsets of the LAION dataset, which serve as OOD counterparts to ImageNet and DomainNet. This ensures no data contamination, and allows investigation of true OOD generalization capabilities of these models. Their main finding is like CLIP like models to not generalize well when such disjoint sets are constructed carefully. This suggests that while these models appear to generalize, they are merely performing well because they have already seen data close to the test set.", "questions": "1. Is there a way of automating the dataset slip so as to train the model on a large scale dataset?\n\n2. Would it make sense to generate \"un-natural\" images i.e. data that does not relate to real world categories to explore this further. For instance, maybe texture images generated from explicitly disjoint data distributions?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "Here’s a refined take on the strengths for your review:\n\n1. **Extremely important problem**: This paper addresses a critical issue in machine learning—distinguishing true OOD generalization from data contamination—by revisiting fundamental challenges of OOD generalization in the context of large-scale vision-language models. Their work highlights the limitations of current evaluation practices, underscoring the importance of ensuring genuine robustness and generalization capabilities.\n\n2. **Impactful finding**: The finding that models like CLIP may not truly generalize when faced with rigorously OOD data is significant, as it challenges current assumptions about the robustness of these models. This insight has substantial implications for future work, suggesting that existing performance metrics may overstate the true generalization abilities of these models.\n\n3. **Useful datasets**: By creating the LAION-Natural and LAION-Rendition datasets—both strictly OOD to ImageNet and DomainNet—this work offers a valuable resource for researchers focused on OOD robustness. These carefully constructed subsets provide a way to assess and enhance model generalization on web-scale datasets without contamination.\n\n4. **Useful methodology**: The methodology for creating disjoint, OOD subsets of LAION is a wel established approach in the field, and a solid blueprint for other research seeking to evaluate OOD performance rigorously. \n\n5. **Results/Figures/Tables are very high quality**: The results, figures, and tables presented in the paper are meticulously crafted, enhancing the clarity and interpretability of the findings. They effectively illustrate the study's key points and provide a solid foundation for future discussions on OOD generalization and robustness evaluation.", "weaknesses": "1. **Missing Literature:** The field of OOD (out of distribution) generalization is very rich, and there are several related papers that are not cited. It would be nice to have this work related to existing works in the field (See below)\n\n### References\n\na. Liu, J., Shen, Z., He, Y., Zhang, X., Xu, R., Yu, H. and Cui, P., 2021. *Towards out-of-distribution generalization: A survey*. arXiv preprint arXiv:2108.13624.\n\nb. Koh, P.W., Sagawa, S., Marklund, H., Xie, S.M., Zhang, M., Balsubramani, A., Hu, W., Yasunaga, M., Phillips, R.L., Gao, I. and Lee, T., 2021, July. *Wilds: A benchmark of in-the-wild distribution shifts*. In International conference on machine learning (pp. 5637-5664). PMLR.\n\nc. Madan, S., Henry, T., Dozier, J., Ho, H., Bhandari, N., Sasaki, T., Durand, F., Pfister, H. and Boix, X., 2022. *When and how convolutional neural networks generalize to out-of-distribution category–viewpoint combinations*. Nature Machine Intelligence, 4(2), pp.146-153.\n\nd. Gulrajani, I. and Lopez-Paz, D., 2020. *In search of lost domain generalization*. arXiv preprint arXiv:2007.01434.\n\ne. Madan, S., You, L., Zhang, M., Pfister, H. and Kreiman, G., 2022. *What makes domain generalization hard?* arXiv preprint arXiv:2206.07802.\n\nf. Arjovsky, M., Bottou, L., Gulrajani, I. and Lopez-Paz, D., 2019. *Invariant risk minimization*. arXiv preprint arXiv:1907.02893.\n\ng. Arjovsky, M., 2020. *Out of distribution generalization in machine learning* (Doctoral dissertation, New York University).\n\n2. **Smaller training dataset size:** One of the key claims in papers presenting vision language models is the idea of Scaling Laws---behavior emerges from large scale datasets which cannot be seen in smaller models. Here, CLIP was trained on 19,000 images which is too small and thus it remains unclear if such generalization errors will continue in extreme dataset sizes. If nothing else, it is worth discussing this point in the paper.", "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": "up6kp0rapw", "reviewer": "Reviewer_r683", "summary": "This work focuses on studying the out-of-distribution generalization capabilities of the CLIP model. To do so, authors consider the scope of *rendition* images and propose to use a domain classifier to detect images from this particular domain from the LAION dataset used to train the CLIP model. By training CLIP with this non-contaminated version of the training data, authors showed that the performance of the model on rendition images decayed, indicating that the original model performance on these images could be explained by the presence of such examples in the training set.", "questions": "- Q1: Even after reading the manuscript a few times, the main goal of this work is unclear to me. Previous work on the domain generalization setting has shown that neural networks do struggle to generalize across domains [1], even with powerful regularization and data augmentation approaches to mitigate domain shift. Given that, why exactly would CLIP be expected to generalize well OOD with limited training data? Given that, it seems to me the result/motivation presented in Figure 1-D is exactly what is expected to be given the IID assumption. Can the author kindly elaborate on their motivation? \n\n- Q2: Authors mentioned in line 62 that \"Domains, while not always rigorously defined, emerge from collecting data from specific sources\nand conditions.\" However, there is a lot of effort from the machine learning to rigorously define what a domain is. For example, the authors can refer to the work by Ben David et al. [2]\n\n- Q3: Details about the annotation task are missing, please add the following information to the manuscript: how many annotators were considered, inter-rater agreement, how annotators were chosen and compensated, description of the task set up.\n\n- Q4: The authors mentioned in Line 257: \"Since human annotators can make mistakes, and LAION-200M’s domain\ncomposition is inherently unknown, we use our domain classifiers to understand it.\". However, I am confused by this statement. The domain classifiers the authors refer to were trained on human-labelled examples, which means they can also inherit the mistakes made by annotators, this approach also seems to be potentially affected by human mistakes (as well as the classifier's mistakes).  \n\n- **Comment that has not affected my score:** The choice of paper title seems odd to me. The title is quite similar to the reference \"In search of lost domain generalization\", which can be confusing on its own, but the both papers are also considerably different in the questions asked and methodology, making it even more odd that they have such similar names. Moreover, the title \"In search of forgotten domain generalization\" does not reflect well the content of this submission as it gives the impression that the contributions are broader than they actually are, since this work only focused on the CLIP model and a specific type of domain shift.     \n\n\n[1] Gulrajani, Ishaan, and David Lopez-Paz. \"In search of lost domain generalization.\" arXiv preprint arXiv:2007.01434 (2020).\n\n[2] Ben-David, Shai, et al. \"A theory of learning from different domains.\" Machine learning 79 (2010): 151-175.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- S1: This work raises awareness of the importance of curating and performing in-depth analysis of large-scale datasets.\n\n- S2: One of the main contributions of this work is to release two curated partitions of the LAION dataset with non-overlapping domains. This will promote and facilitate future research on single-source domain generalization for foundation models such as CLIP.", "weaknesses": "- W1: The motivation for the main question this work aims at answering (\"shed light on the limitations of foundation models like CLIP in handling OOD generalization\") is not quite clear from the manuscript. In a few words, it seems this work is showing that when CLIP is trained and tested on the same distribution, it performs well, and performance is harmed once a particular domain is removed from the training set and the model is tested on it. What is the exact new insight from this result? Please clarify this in the manuscript.\n\n   - W1.1: In a similar vein, previous work [1] has pointed out that CLIP has limited OOD generalization capabilities and also proposed fine-tuning approaches to mitigate such issues.\n\n- W2: The approach used for data curation relies on collecting human-labeled to train a domain classifier, making it specific to the setting studied in the paper, i.e. rendition vs natural images. \n\n- W3: In Table 2, the authors report the number of natural/ambiguous/rendition images resulting from the filtering with the domain classifier. However, it is possible to see that in most of the cases a considerably high number of images (> 60% in some cases) is deemed as ambiguous. This seems to indicate that either such domain classifier is not a good model to distinguish natural vs rendition images, or that it could be the case that it does not make sense to consider both domains as two distinct sources of data.\n\n- W4: Lack of overall clarity. Some statements such as \"We conclusively demonstrate that domain contamination in the training set is what drives CLIP’s robustness\", however, if rendition data is present in the training data, then rendition data is no longer OOD with respect to the training distribution. \n\n\n[1] Shu et al, CLIPood: Generalizing CLIP to Out-of-Distributions, ICML, 2023.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "c8HN0y5MwQ", "reviewer": "Reviewer_Gz5f", "summary": "This paper reintroduces the OOD problem in the era of the foundation model. They propose the argument that the phenomenon that foundational models are no longer  susceptible to OOD samples is merely an illusion due to the *domain contamination*, which they define as \"whether crucial aspects of a test domain are included in the training domain, e.g., by including images with different content but similar style to test samples\"\n\nThey then introduce cleaned single-domain datasets to study this problem, discovering that CLIP trained only on natural images significantly underperforms on rendition domain shifts. They further investigate the domain mixing and scaling that affects OOD performance.", "questions": "I have a few more questions that would love to discuss with the author. However, these questions should not count towards the evaluation of the paper.\n1. Do you think the improved generalization comes from merely more data points or a more sophisticated loss or learning paradigm (i.e. contrastive learning and aligning with language)\n2. Despite this paper is mainly on CLIP and contrastive vision language model, the main-stream of foundation models are generative models that are instruction-tuned and aligned. Do you think this work and its conclusion will generalized to LLMs? Specifically, one interesting new thing that was not used in CLIP training is the instruction tuning that enables the model to generalize across tasks. In that sense, models will learn to generalize across tasks instead of data points. For example, MLLMs can be trained to generalize to the task of discriminating between any two domains given by the user. Given such ability, will the MLLMs have better OOD generalization?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. This paper studies a valuable problem that has seldom been studied. Indeed, the OOD problem, which seems to be less of a problem in the era of LLM, might be an illusion due to the scaling of the massive data. This problem should be taken seriously, as there are still areas where collecting data is extremely difficult and scaling will likely fail.\n2. Sound analysis that supports the claim.", "weaknesses": "1. Figure 6 A is a bit confusing, as the result of the best rendition-to-natural ratio 1:3 and 1:1 can not be read in this figure. I suggest adding ratio labels to the color scale or individual data points in the figure.\n2. Can you provide some discussion on the \"true effectiveness\" of the domain classifier trained and evaluated on the curated domain datasets but is used in a different and much larger dataset, i.e. Laion-200M?\n    * Will there be OOD shift between the curated domain datasets and Laion-200M?\n    * If so, how to make sure the domain classifiers are good?\n    * Maybe you can sample a few classified samples in Laion-200M and verify the result by humans.\n    * Will adding more domain classifiers and performing an ensemble help?\n    * I suggest that the authors include a dedicated subsection in their methodology or results discussing the domain classifier's performance on LAION-200M, including human verification of a sample of classifications and exploration of ensemble methods.\n\n3. Some typos, e.g data points instead of datapoints on lines 76, 82, etc.\n4. Overall, I believe this is a technically sound paper with rigorous analysis that supports its main argument.", "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": "irBPmkSYOw", "reviewer": "Reviewer_8EXX", "summary": "The paper focuses on exploring the phenomenon of test domain contamination in the era of foundation models. The paper constructs two different style datasets, LAION-Natural and LAION-Rendition, which are strictly OOD relative to the corresponding ImageNet and DomainNet test sets in terms of style. It examines whether the good OOD performance of CLIP results from test set contamination or from its training paradigm. The findings indicate that CLIP's success stems from domain contamination rather than an intrinsic OOD generalization ability. Additionally, the authors suggest how the mixing ratio of Rendition and Natural samples in the training set can optimize model performance on the test set.", "questions": "No", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. This paper gives a very detailed analysis that clarifies that the success of CLIP mainly comes from the training data rather than its own training paradigm, which can give some inspiration to many researchers.\n\n2. The authors give the optimal mixing ratio of rendition and natural image, which allows for better generalization performance of models trained on this dataset.\n\n3. The authors conduct extensive experiments and analyses to support the claim that \"the success of the base model in OOD settings does not originate from its own characteristics\", which enhances its credibility.", "weaknesses": "1. The main issue with this paper is that it appears to draw a conclusion \"he success of CLIP mainly comes from the training data rather than its own training paradigm\" that lacks sufficient insight for subsequent researchers. While the question of the optimal ratio of natural and rendition images in the training set is indeed illuminating, the authors do not seem to have conducted tests with models of different architectures. Additionally, I believe that this experiment is important because it can ensure that the optimal ratio is not sensitive to the model's architecture.\n\n2. Although I have experience in OOD research, I am not up to date with the latest techniques and conclusions in this area. Therefore, I will be observing the perspectives of other reviewers as well as the authors' responses. Overall, the paper was thorough and solid, but it seemed to lack novelty.for me.", "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 (Spotlight)