An Analysis of Model Robustness across Concurrent Distribution Shifts
Authors: Myeongho Jeon
Suhwan Choi
Hyoje Lee
Teresa Yeo
Venue: Accepted by TMLR ()

Abstract: Machine learning models, meticulously optimized for source data, often fail to predict target data when faced with distribution shifts (DSs). Previous benchmarking studies, though extensive, have mainly focused on simple DSs. Recognizing that DSs often occur in more complex forms in real-world scenarios, we broaden our study to include multiple concurrent shifts, such as unseen domain shifts combined with spurious correlations. We evaluate 26 algorithms that range from simple heuristic augmentations to zero-shot inference using foundation models, across 168 source-target pairs from eight datasets. Our analysis of over 100K models reveals that (i) concurrent DSs typically worsen performance compared to a single shift, with certain exceptions, (ii) if a model improves generalization for one distribution shift, it tends to be effective for others, (iii) heuristic data augmentations achieve the best overall performance on both synthetic and real-world datasets.

Published in Transactions on Machine Learning Research (01/2025)
An Analysis of Model Robustness across Concurrent Distribution Shifts
Myeongho Jeon"
myeongho.jenn@eepffcc
École Polytechnique Fédérale de Lausanne
Suhwan Choi' Scoul National University CRABs.ai
schoi828 ac.
Hyoje Lee Samsung Research
hgooee.ler@samsuungccom com
Teresa Yeo
teresa. Geresa.yeo@smaar..oo @ssmart.
edu
Singupore-MIT Alliance for Research and Technology
Reviewed on OpenReview: https: oppenrevee,,. net/ wet/teetteeeeeee forum? id-
Code: https: // gethub. com/ sttp:/ccooc8////00 robus tness
Abstract
Machine learning models, meticulously optimized for source data, often fail to predict tar- get data when faced with distribution shifts (DSs). Previous benchmarking studies, though extensive, have mainly focused on simple DSs. Recognizing that DSs often occur in more complex forms in real-world scenarios, we broadened our study to include multiple concur- rent. shifts, such as unseen domain shifts combined with spurious correlations. We evaluated 26 algorithms that range from simple heuristic augmentations to zero-shot inference using foundation models, across 168 sourco-target pairs from cight datasets. Our analysis of over 100K models reveals that (i) concurrent DSs typically worsen performance compared to single shift, with certain exceptions, (ii) if model improves generalization for one distribu- tion shift, it tends to be effective for others, and (iii) heuristic data augmentations achieve the best overall performance on both synthetic and real-word world datasets.
1 Introduction
Machine learning models deployed in real-world settings may face complex distribution shifts (DSs). These shifts result in unreliable predictions. For instance, self-driving may be deployed in place where there is different driving etiquette, unfamiliar environment and weather condition. Thus, systematic evaluation of such complex shifts is essential before deploying models in the real world.
Most research has focused on evaluating models under a single DS (UniDS) Taori et al. 2020 Gulrajani &
) For instance, in the PACS evaluation benchmark (Li et al., 2017), the training data consists of photographs, but the model is tested on sketches. Similarly, in the training data of Biased FFHQ (Lee et al. 2021), gender is data where gender is anti-correlated to age. In encounter these two shifts concurrently, i.e., one where age and gender are
Lopez- 2020: Koh et al.] 2021 Wiles et al. 2022 Miller et al. 2021 Wenzel et al. 2022
spuriously correlated to age, but the model is tested real-world scenario, spuriously correlated and where there is also shift in the style of the image.
"Equal contribution.
i "oorreppondigg author.

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Image attributes
Using single attribute create DS
Using multiple attributes to create DS
Train
Attribute used to DS:
Train
Attributes used to create DS: Background colox Object color
Backgzound
Object ahape: Square
Background coloz
SC+
Object poaltions Sep right
LDD
Background color: Furple
Test
dbject shape: pillpse
Object colort Red
object poaition: Cep left
SC+
Background color: Crange
UDS
object shape: leart
Object color: Yellev
object position: Button left
Background color: Green object stape: Beart
LDD+
UDS
Object positions left
Figure 1: Concurrent distribution shifts. Left: We list some attributes of few images from the dSprites dataset. In this dataset, the object shape is the label. Center: We show how single attribute e.g,, the background color, can be used to create different types of distribution shifts. Namely, spurious correlation (SC), where in this example, the background color is correlated with the object shape, low data drift (LDD), and unseen data shift. (UDS). We assume that the lest data consists of images where all attribute instances are equally likely to appear, i.e., each image generated by randomly selecting each attribute instance with equal probability. Right: As the real world consists of more complex shifts, we also make of multiple attributes to create combinations of distribution shifts. In the examples above, object color to create combinations of distribution shifts. The first shift is created using the background attribute and the second shift, the shape attribute i.e., SC+UDS is created from a correlation between the background color and the shape (SC), and only using a subset of colors, for the shape altribute (UDS).
the background and
To address this, we introduce an evaluation framework that mirrors the complex DSs potentially found in real-world settings. We assume that the input is generated from a set of attributes, e.g., gender, age, image style, etc. Thus, we leverage existing multi-attribute datasets, such as CelebA (Karras et al. 2017). which includes 40 attribute labels such as gender, hair color, and smiling, to manipulate the type and number of DSs present in the dataset. We refer this framework as ConDS. can account for the co- occurrence of different DSs across multiple attributes within paired source. and target datasets. An example of this protocol is illustrated in Figure [ Consoquently, evaluating existing methods against these shifts provide insights unattainable with current benchmarks. Specifically, proposed framework consists of the following components:
Distribution Shifts. We explore seven types of DSs, including spurious correlation (SC), low data drift (LDD). unseen data shift. (UDS), and their combinations; that is, (SC, LDD), (SC, UDS), (LDD, UDS), and (SC, LDD, UDS). To offer a comprehensive analysis covering a wide range of cases, we select various attributes to induce DSs. This includes adjusting three attributes for each UniDS and crcating six combinations from three attributes for each ConDS, resulting in 33 distinet cases per dataset.
Algorithums. We evaluate 26 different methods from 7 popular approaches: architectural strategies, data augmentations, de-biasing, worst-case generalization, single domain generalization, out-of-distribution gen- eralization, and even zero-shot inference using vision-language foundation models.
Experiments. We evaluate these algorithms on all proposed combinations of DSs. This results over 100K experiments across 168 dataset pairs sourced from 3 synthetic and real-workd benchmarks. Further, we examine aspects such as the effectiveness of pre-training and the influence of prompts foundation models for image classification.
As a result, we have made some intriguing findings: (i) Concurrent shifts are generally more challenging than single shifts. However, in ConDS, when a particularly difficult DS combined with relatively easier ones, the harder shift tends to dominate, limiting any further increase in overall difficulty. (fi) If model improves generalization for one DS, it proves effective for others, even if it was originally designed to ad- dress specific shift. (iii) Heuristic augmentation techniques outperform meticulously crafted generalization
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methods overall. (iv) Although vision-language foundation models (e.g., CLIP, LLaVA) perform well on simple datasets, even in the presence of DSs, their performance significantly deteriorates more complex, real-world datasets.
2
Related Work
In this section, we review existing benchmarks for distribution shifts (DSs) and frameworks for evaluating model generalization.
Benchmarks. Several types benchmarks have been introduced to evaluate the generalization of models. One lype of benchmark evaluates generalization on datasets collected from different sources, i.e. due to
different domains {Li et al. 2017 Venkateswara et al., 2017 Peng et al. 2019 Koh al.) 2021 e.g., the train and test data are collected from different countries, or different time periods (Yao al. 2022 Hendrycks
et a.. Sagawa et al. 2019 Nam et al.) 2020: Bahnn et al. 2020 Jeon et al. 2022b). Such transformations range from analytical ones like rotation, corruptions like "brightness" or 'contrast', to learned ones like adversarial attacks. Images with the same transformations form a domain, while each transformation can be an attribute, creating spurious correlations by matching labels to transformations. Other alternatives includes using engines like Blender or Unity to create simulators that can render different types of shifts (Leclerc et al. 2022 Sun al. 2022). In contrast, |Rectt al. (2019) collects new test sets for ImageNet and CIFAR-10 by replicating the data collection pipeline. They showed that without any explicit distribution shift, there is drop in accuracy. Similarly, [arbbu et al. (2019) collects a new dataset where objects have unusual poses viewpoints. The benchmarks most similar to ours involve changing the original train-test split of the dataset to induce different types of DSs (Kim et al.) 2019 Koh et al. 2021 Jeon et al. 2022a Atanov et. al. 2022 Jeon et al. 2022b). In contrast to these methods, our paper focuses on creating controllable concurrent shifts by using attribute annotations in existing datascts.
2021 Another popular type applies transformations to the input (Hendrycks & Dietterich 2019;
Large-scale Generalization Analysis. Although methods that enhance robustness against DSs have been extensively rescarched, significant variations across application domains mean that method that excels in one datasel might not perform equally well in another. Consequently, recent efforts have been dedicated to comprehensively and fairly evaluate generalization methods. Gulrajani & Lopez- Paz (2020) demonstrated that empirical risk minimization, when meticulously implemented and finely tuned, excels over domain generalization methods in robustness. Taori et al. (2020) found that generalizability to syn- thetic shifts does not guarantee robustness to natural shifts. Koh al. (202)) introduced Wilds, a curated benchmark consisting of 10 datasets that encapsulate a diverse range of DSs encountered in real-world tings. Their findings indicate that current methods for generalization are inadequately equipped to address real-world DSs. Wiles et al. (0022) reported that both pre-training and augmentations significantly boost performance across many scenarios, although the most effective methods vary with different datasets and DSs. Additionally, Miller et al. (2021} and Wenzel et al. (2022) observed a positive correlation between out-of-distribution and in-distribution performance. However, these studies predominantly concentrate on single DSs, which do not fully capture real-world scenarios. Ye al. (2022) categorized existing benchmarks based on the extent of spurious correlations and the degree of domain shift. Their findings reveal that, for the most part, Out-of-Distributiin generalization algorithms remain susceptible to spurious correlations.
3 Problem Statement
In this section, we introduce the concept of distribution shift (DS) and its two categories: unique distribu- tion shift (UniDS) and concurrent distribution shift (ConDS). To operationalize the concept of ConDS, annolations for multiple attributes are required. For clarity, we begin by introducing one of our experimental datasets, dSprites, in Figure 2
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3.1 Distribution Shift
Consider instance set X {x|/x; X NJ for a classification problem, where X' de- notes the input space. Each instance x can be repre- sented by a finite set of attributes Y(x) = {y"ly E Vi 1,..., K where y denotes the attribute space and K varies with x. Within this framework, one at- tribute y' from (x)) can be designated as the label. Let P. Ps. and Pr be the true, source, and larget dis- tributions, respectively. We denote the source and tar- get datasets by Ds ps and Dr ~ pr, respectively, where each dataset consists of samples from its corre- sponding distribution. machine learning model f is designed to minimize the empirical risk,
y": object shape
yo: object color
yº: background color
y" object size
Figure 2: dSprites samples. Even in simple syn- thetic data, multiple attributes can potentially lead to various DSs. Visualizations for other datasets are included Figure 5 in the Section A.1
f f== arg min
1 N
Σ
(1)
where (x,, Ds for -,..., N. The idcal objective to find the model f that maximizes its performance true distribution p(x, y); that is,
fr arg min Bp(x, [el'' f(x))
(2)
However, significant shift in distribution from source to target data, denoted by Ds &6 Dr, can cause a substantial decrease in performance during inference. Assuming that the target distribution pr aligns with p. we define DSs the following subsections.
3.2 Unique Distribution Shifts
We revisit spurious correlation (SC), low data drift (LDD), and unseen data shift (UDS) as delineated the experimental framework by Wiles et al. (2022), grouping these under the category of unique distribution shifts (UniDS), namely UniDS {SC, LDD, UDS}. To create the UniDS, we select one attribute from the candidates (y*, y y²) and then sample the images for each DS accordingly. All other attributes display uniform distribution for UniDS.
Test distribution Pr- All the attributes yd E (x) are uniformly distributed, ensuring that each attribute is represented and independent. from the others. For example, for pr s there is an equal number of samples across 81 combinations, ranging from (square, red, orange, small) to (heart, blue, purple, big).
Spurious correlation. Under Ps. the label yf and specific attribute y" exhibit correlation, which does not hold under pr. For example, there are only (square, red), (ellipse, yellow), and (heart, blue) samples in Ds, exhibiting correlation between 'shape' and 'color'.
Low data drift. Attribute values show an uneven distribution under ps but are more evenly distributed under PT. Generalization for LDD is also referred to as worst-case generalization (Sagawa et al. 2019 Seo 2022 For example, there are significantly fewer red samples compared to the numerous yellow and ct al.
blue samples. Even among the red samples, the distribution of shapes uneven.
Unseen data shift. Some attribute values that are present under Ps appear under pr. E.g., There are blue images in Ds, indicating that blue images in Dr are new to the model.
3.3 Concurrent Distribution Shifts
Previous benchmarking studies have predominantly focused on UniDS. However, in real-world contexts, dataset. could contain concurrent DSs. Given that instance encompasses numerous attributes, DSs
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can exhibit more complex structures, with each attribute governed by distinct UniDS. For instance, in the real-world dataset iWildCam, UDS are observed due to discrepancies in the camera traps used across source and target data. Concurrently, LDD emerges from variable image counts across animal classes. Therefore, to effectively assess the efficacy of methods in real-world applications, it is crucial to conduct a comprchensive evaluation that addresses these scenarios.
To reflect the complexities observed in real-world datasets, we introduce the concept of "concurrent dis- tribution shift (ConDS)'. This novel framework extends conventional UniDS by modeling the diverse shifts associated with different attributes of dataset. In this study, we focus combining various shifts attributed to different attributes, rather than evaluating a single type of shift across multiplc attributes- topic extensively covered in previous literature.
In our formal definition, ConDS is conceptualized as the ensemble of all possible combinations of two or more distinct UniDS elements,
ConDS == (S C UniDS |S| 2 2),
(3)
where each subset S represents a unique configuration of shifts, creating a richer and more representa- tive model of the underlying complexities in rcal-world data. Each pair (y', y²) within this framework, where & belongs to a predefined set {a, 3, 7), governed by specific UniDS.
For instance, using the attributes shown in Figure (SC, UDS) can be established by sampling combinations such (square, red), (ellipse, ycllow), and (heart, blue), while the background color varies between (orange, green) and the object size maintains uniform distribution. The concept of ConDS and its importance were also introduced in Koh et al. (2021). However, their discussion was limited to LDD and UDS. We broaden this to include SC, LDD, and UDS, aiming for a thorough understanding of their interconnections. Additionally, we investigate broader array of algorithms to assess how existing methods perform on ConDS.
4 Generalization to Distribution Shifts
As discussed in Section 3.1 it is assumed that during training, we only have access to source data, while the
target distribution remains unknown (Vapnik 1991 Jeon et al.
2023 While some algorithms are specifically
designed for scenarios where partial knowledge of the larget distribution is available Adeli et al. 2021 Alvi
et al. 2018: Kim et al. 2019 Geirhos et al., 2018 Bahng et al. 2020 Ganin et al. 2016), the setup where no
target information is known generally poscs broader challenge for model gencralization. Consoquently, our analysis focuses on this more general setup. We evaluate 26 algorithms suitable for this scenario, spanning wide spectrum of methods, as shown in Table
Table 1: Generalization algorithms evaluated. We outline the methods evaluated and the distribution shifts they are designed to address.
Generalization Algorithma
SC
LDD
UDS
18 NesNet50, ResNet101
He al 2016
Architecture
VIT Dosovitskly
2020
MLP Vapmi
Imagenet He
2016
AugMix Henddyckk
2019
Heuristic augmentation
RandAug
Cubuk
2020
AutoAug Cubuk
2019
UBNet Jeon et
2022a
PuD
Lá al. 2023
De-biasing
Occamiets Shrestha al.
2022
Worst-ease generalization
GroupDRO Sagawa
2019
BPA Sro al. 2022
ADA (Vopii et al.
2018
ME-ADA ZZhao el al. 2020
Single domain generalization
SagNet Nam
2021
L2D Wang et al. 20/1
Ont-of--istriinttion generalization
IRM Arjovsky et al. 2019
CansIRI. Chevalley
al. 2022
CLIP [Radford et al
2021
InstrretBIIP
Dai et al
2024
Zero-shot inference with foundation model
LLaVA (Tau et al.
W223
vhi-on5-vision Abdin et al.
2224
GPT-4o-min, GP1-40
OpenAl
2024
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Augmentation for generalization. Although some augmentation techniques not specifically designed for robustness, they are commonly used to enhance generalization against. known for their effectiveness (Yan et al.) 2020; Wiles et al., 2022] Cugu et al. 2022] ZZheng et al. 2024). The operative idea is that augmentation expands the input space, thereby increasing the likelihood that the model will recognize new lest samples.
the unseen domain and are well
Generalization algorithms. De-biasing: UBNet utilizes feature maps from lower to higher layers to enable the model to access a broader feature space. PnD removes spurious correlations by detecting them using GCE (Zhag & Sabuncu 2018). OccamNets employs architectural inductive biases, demonstrating their effectiveness in mitigating spurious correlations. Worst-case generalization: GroupDRO and BPA modify the weight of gradient flow to ensure balance across different groups. Single domain generalization: ADA and ME-ADA generate additional inputs by incorporating adversarial noise. SagNet is designed to be agnostic to styles, emphasizing content instead. L,2D utilizes learned augmentations with AdaIN from StyleGAN (Karras generalization: IRM and CausIR climinate variant features by regularizing discrepancies among samples from the source data.
ct al. 2019).
Foundation models acquire robust representations from comprehensive datasets, facilitating their application in downstream tasks such as image classification. The mechanisms for aligning inputs to outputs (X' - >) during zero-shot inference differ among models. CLIP utilizes the similarity between image embeddings and textual label embeddings to calculate confidence scores. Conversely, InstructBLIP, LLaVA-1.3 Phi-3.5-vision, and GPT-40 employ label-eliciting prompts as queries within the framework of visual question answering (Chen al.) 2022). Additional details about the prompts used for image classification can be found in Table 12 of the Section
Foundation model for image classification.
B.5
5 Experiments
In this section, begin by presenting the datasets we evaluated and the experimental setup. Next, we evaluated 26 distinct algorithms across 168 (source, target) pairs spanning six datasets, addressing both UniDS and ConDS, The aggregate results are illustrated in Figure 3 while the comprehensive results are detailed in Section B.1 We further examine how the challenges vary among different DSs (Figure 4 and evaluate the effectiveness of pre-training in enhancing robustness (Table 3 Further analysis investigates how zero-shot inference performance depends on distribution shifts (DSs), as shown Table Finally, we analyze the outcomes, summarizing them into eight key lakeaways.
5.1 Datasets
Controlled datasets: We assess algorithms using five evaluation datasets: dSprites, Shapes3D, Small- Norb, CelebA, and DeepFashion. From these, we select four attributes: one is designated as the label (v"), and the other three as attributes (y², y², y²) to create DSs. Table 2 lists the attribute instances for y and {v" .r.,y, y"} } for the different controlled datasets. We divide the source data into training and validation sets, both sharing the same distribution, with the validation set used for hyperparameter tuning.
Uncontrolled real-world datasets: We use iWildCam, fMoW, and Camelyon17 for evaluation. iWildCam data in the Wilds benchmark (Koh et al. 2021) exhibits LDD over the animal distributions, and UDS occurs across camera trap locations. Similarly, the fMoW dataset (Koh et al., 2021 exhibits UDS and LDD across time and regions in satellite images. Camelyon17 is a tumor detection dataset with various unxxpectdd DSs resulting from different hospitals. We further discuss additional insights from the catego- rization of synthetic (Dsprites, Shapes3D, Smallnorb) and real-world datasets (CelebA, DeepFashion, iWildCam, fMoWW Camelyon17) in Section A.3
5.2 Experimental Setup and Results
Controlled datascts: To comprchensively understand the generalizability of the algorithms on diverse DSs, we evaluate with three different seeds for five datasets changing attributes fy", y.". y").. Specifically, with labels fixed to y², manipulate attributes to simulate different DSs. For instance, we select one attribute
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Table 2: Attributes for controlled datasets. Each dataset's attributes and their possible values, Image samples from each dataset are displayed in Figure in Section A.1
Dataset
Attribute
Values
8/
object shape
(square, ellipse, heart) red,, yellow, blue]
1/
object color
dSprites
background color
(orange, green, purple) (small, middle, big)
97
object size
gf
object shape object color
(cube, cylinder, sphere, capsule)
y
(red, orange, yellow, green]
Shapes3D
g
background color
(red, orange, yellow, green) (tiny, small, middle, big)
y
object size
y
object
(animal, human, truck, airplane) (0, 80, 160, 240, 320} (0, 1, 2, 3, 4] [30, 40, 50, 60, 70)
yo
szimuth
SmallNorb
lighting
y7
elevation
g
gender
(male, female]
yo
hair color
{black, others)
CelebA
smiling hair style
(smiling, smiling)
y
(straight, others)
y
dress
(skirt, others}
yo
pattern
(ftrrl,, solid)
DeepFashion
g
sleeve
(long sleeve, sleeveless) (chiffon, cotton)
fabric
from Ey" y y to create UniDS (3 settings). For ConDS, we use six combinations (either 3C²2 or 3C's settings) to establish designated DSs, resulting in a total of 165 (source, target) pairs. For example, for the (SC, LDD) setting, we choose one attribute define SC and another from the remaining two to create LDD, resulting (source, target) pairs. The detailed description for this provided in Section A.1 For all SC,
Architecture
Augmentation
De: bias
Worst
SDG
00D
Zero-shor
UNIFORM
SC
0.2
LDD
UDS
SC+LDD
0.0
SC+UDS
LDD+UDS
LCDI UDS
0.2
Reallec0
Besllet101
XL.
Isagoliet
Aagliix
RandAug
Autolag
UBllet
Pab
Occantlete
GroupDRO
BPA
NE- ADA
Baglitt
TRM
CausIRL-H
CauaTRI-C
CLIP-ba CLiP-base base
InstructBLIP
LLaVA-1.5
Phi-3 5-vision
OPT-4ommnn mini
GPT-4p
CLIP
Figure 3: Aggregate result on controlled datasets. We plot the change in accuracy compared to the base model, ResNet18, averaged across all seeds and controlled datasets with varying attributes. Blue indicates improved performance, while red indicates a decline. Each row is independent of the others. The models used for zero-shot inference were only used for evaluation, thus, they have the same absolute performance for each row. However, as we show their accuracics relative the ResNet18, the relative performance for each model is the same for each row. We fine-tura all the algorithms and report their optimal results. Augmentation methods and zero-shot models perform well under the different types of shifts. We provide breakdown of the accuracy for each algorithm and dataset in the Section B.I

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Table 3: Training from scratch vs Pre-training. All the values reported for controlled datasets represent the average accuracy across all DSs and controlled datascts. We compare the performance of models trained from scratch with those pre-trained, bolding the better result between the two training strategies. The top average result in each column except zero-shot inference is indicated with a gray box, while the second-best is underlined. We used the original models for zero-shol inference for evaluations, thus, they are neither trained from scratch nor fine-tuned. Trends on controlled datasets us Real-world datasets. We also show the results from evaluating on iWildCam, fMoW,, and Camelyon17, as they are challenging real-world datasets with ConDS. The experimental results from our controlled datasets align with those from the real-world datasets, demonst that even on real-world datasels, augmenlation and pre- pre-training significantly enhance robustness.
Controlled Dataset
Real-world Dataset
Seratck
Pre: tecining
Sentthh
Pre Fre-truinig
ResNe118
02.43(=0.84)
81.53(10.67)
53.91(42.19)
61.64(+1.71)
ResNet50
60. 60.13(=0.85)
82.64(=0.62)
52.58(+1.86)
67.76(+1.46)
Architecture
ResNet111
56.98( 0.888
79.96(+0.69)
47.01(+2.09)
67.52(+1.41)
VIT
19.55(=0.60)
(8.63(+0.62)
50.55(42.11)
70.94(11.55)
avg.
07.27(-2.81)
80.67(1:0.00)
51.01(+1.50)
06.97(=1.94)
ImtgeNet
09.25(=0.88)
80.55(=0.64)
48 1.)))
66.18(11.60)
AugMix
68.99(-0.83)
83.15(+0.56)
51 +1(97)
68.09(+1.53)
Augmentation
RandomAug
71.47(00.86)
85.28(10.53)
51.94(11.84
68.29(11.62)
AutoAug
69.65(
(5.85(+
52.15(+1.63)
70.29(+1.59)
ang.
69.8 (=0.6))))5))
83.71(+:1.20)
51.22(+:0.80)
(8.29(=0.78)
UBNet
61 0..66)
78.26(+0.67)
41.01(++.32))
59.03(+ 11.77)
PnD
08. 08.87)
(7.67
50.02(422.00
60.34(+1.55)
De-biasing
OccamNets
61.98(00.71)
(7.59(=0.68)
52.57(++1.0)
25.20(++1.39)
ang.
64. (-2.4)))
77.84(+-0.21)
(7.87(+-3.61)
61.52(=1.88)
GroupDRO
63.26(-0.84)
81.23(+0.67)
48.17(+1.87)
64.40(+1.22)
Worst-case generalisation
BPA
(5.69(=0.74) 50.99(=0.81)
40.4((33.98)
62.15(+11.6)
avg.
61. 61.63(11.63)
78.46 77)
(4.80(+3.888
63.28(=1.13)
ADA
02.96(=0.82)
80.20(10.68)
52.88(+2202)
69.71(++1.51
ME-ADA
(8.33(+ 62.37(=0.82)
53.79(42.04)
66.41(++1.20)
Single domain generalization
SagNet,
61 (1.09(00.86)
$1.56(+0.67)
54.27(+1.93)
66.75 (1277
L2D
(6.4(()0.53)
82.63(10.60)
52.67(42247)
68.20(11.95)
ang.
63.22(- 1.14)
80.68(++0.93)
58.40(+ 0.308)
67.77(-0.75)
IRM
69.83(=0.83)
(0.276)))688)
39 (74-2.4)))
57.90(+2.14)
CausRLI-M
0..55(=0.88)
80.81 (+).75)
18.90(++1.97)
65.87(+1.21)
Out-offdisttiuutiio generalization
CausIRI-C
61.17
82.02 (...8)
54.38(12.07)
68.06(11.55)
ang.
60.52( 1.94)
01.03(+00522)
47.58(+ 4.35)
63.94 3.09)
CLIP-base
78.92
25.76
CLIP Ca.ee-arge
86.22
29.06
InstructBLIP
70.95
29.35
LLaVA-1.5
88.24
Zero-shot inference
Phi-3.5-vision
85.63
GPT-1o mini
83.62
77
GPIT-40
82.57
45.16
avg.
82.51
$2.18
include 1% of counterexamples, similar to the setup established in the research SC robustness {Jeon) et al. 2022a Nam et al.) 2020). We set ResNet18 as the backbone for all algorithms. More details about hyperparameters are provided in Section A.6 Real-world datasets: We adhered to the use of the train, validation, and test sets for iWildCam, fMoW, and Camelyon17. Real-world datasets do not exhibit a clear distribution shift like controlled datasets, but they inherently contain various naturally occurring distribution shifts that may go unnoticed.
Results. FigureBshore the aggregate result of algorithms across DSs for the controlled datasets. Figure S and Table 3 present the outcomes when methods are trained using ImageNet pre-trained weights. In Table 3 some algorithms, particularly augmentation techniques, surpass large models in scenarios without SC, an advantage not. observed when training from scratch (see Figure 3 Table indicates that pre-training
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enhances performance for all the algorithms and DSs. In experiments, while foundation models perform well on controlled datasets, their elfectiveness is often limited when applied to real-world datasets.
Comparison of distribution shifts. In Figure
evaluate the challenges associated with varying mumbers of DSs. To ensure consistent comparison across these shifts, we standardize the sizes of the datasets. However, due to limitations in data availability that prevent standardization while adhering to specific DSs, our analysis primarily relies on the Dsprites and CelebA. As illustrated in Figure | although the overall performance algorithms tends to degrade as the number of DSs increases, SC remains inherently challenging and shows almost no performance decline when combined with other DSs. Additionally, SC is significantly more challenging than other distribution shifts, even when considering multiple shifts combined, such LDD+UDS.
0.75
0.70
0.8-
Acmraey
0.7
UNIFORM
LDD
UDS.
SC-LDD
SC+UDS
LDD+UDS
SC+LDD+UDS
UNIPORM (0)
UNDDS (1)
CorDS (2)
CoNDS (3)
Architecture
Worst-case
Augnentation
De-bissing
OOD
Figure 4: Analysis of model robustness on distribution shifts. Left: Average performance of all generalization methods under different combinations of DSs. Right: Comparing the different generalization methods under an increasing number of DSs.
Table 4: An example of zero-shot inference prompts. The first two rows show the 'General' prompts and the next three rows the "Tailored' prompts that used with LLaVA-1.5. For more comprehensive list of these prompts, please Table 12 in Scction B.5
Type
Prompt
USER: <image>
General,
Classify the image into label....., or labelc. Please provide only the name of the label.
USER: <image>>
Generaly
Choose a label that best describes the image. Here is the list of labels to choose from: labely.... labelc- Please provide only the name of the label.
dSprites
USER: <image>>
Shapes3D
Classify the object in the image into labeli...., or lobele. Please provide only the nome of the label.
SmalINorb
USER: <image>
CelebA
Classify the person image into label, or labelz. Please provide only name of the label.
USER: <image>
DeepFashion
person wearing a or not? Please answer in yes or no.
Further analysis on prompts of foundation model. We investigate various prompts to assess the effectiveness of foundation models for robust. image classification. We assess the accuracy of the models in

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Published in Transactions on Machine Learning Research (01/2025)
Table 5: Comparison of the performance visual language models across different types prompts. We report the best results obtained from this ablation study in Figure g and Table Note how sensitive the results are the prompts used. There can be a drop in accuracy of 37% with suboptimal prompt.
Model
Prompt
dSprites
Shapes3D
SmallNorb
ColebA
DeepFashion
iWildCam
fMoW
Camelyon17
CLIP-base
General
75.19
71.41
72.02
98.5
77.50
13.97
13.30
50.01
CLIP-large
General
90.86
85.39
86.00
97.62
71.25
13.70
23.46
50.01
General
50.25
50.08
26.59
98.63
61.25
1.32
11.61
50.57
InstrueuBIIIP
Generaly
53.46
53.05
36.22
70.50
47.50
1.86
18.17
68.03
Tailored
58.89
74.77
25.41
78.25
86.25
1.10
9.67
50.00
General
49.01
52.89
90.05
98.50
4.64
15.04
50.57
LLaVA-15
Generalg
72.72
74.61
87.81
98.88
91.25
1.60
16.32
51.09
Tailored
86.42
71.02
88.16
98.88
88.75
14.24
50.00
General
88.27
72.73
83.20
98.62
82.50
5.88
9.67
64.86
Phi-3.5
Generala
88.27
72.50
85.41
98.75
78.75
4.06
10.88
57.09
Tailored
88.77
72.58
83.30
98.62
53.75
9.91
9.17
66.71
General,
96.79
94.77
81.12
93.87
50.0
43.43
18.87
64.81
GPT-4o mini
Generaly
92.84
94.77
78.37
93.87
50.0
43.36
19.28
64.87
Tailored
92.84
94.77
82.69
93.87
50.0
41.88
19.89
64.98
General
92.84
94.77
80.22
82.87
50.00
51.64
24.98
58.02
GPT-4o
Generala
92.84
94.77
78.37
93.75
24.80
58.03
Tailored
92.84
94.77
78.85
89.62
50.0
51.21
25.58
58.10
widcly adopted prompts, such "a photo of label' (Matsuura et al.). Given that vision- laiguale models designed for visual question answering may depend on the query's context, the choice of prompts is crucial (Sahoo et al.] 2024). We categorize prompts that are applicable to any dataset as 'General', and those specifically designed for each dataset as 'Tailored'. We provide several examples for the prompt in Table 4 In Matsuura et al. and Islam et al. (2023), diverse prompts are employed as queries for vision-language models, specifically tailored the dataset. and the labels targeted for classification. Our analysis builds on this approach.
Table For CLIP,
5.3 Takeaways
Takeaway 1 ConDS is, on average, more challenging than UniDS. While previous studies on model generalization have primarily focused on UniDS, we observe that most algorithms exhibit poorer performance in ConDS. Moreover, the more numerous the DSs, the greater the challenge they pose, as illustrated in Figure (right).
Takeaway - SC is the most challenging DS, followed by UDS and LDD. Figure 4 (left) breaks down the performance of generalization methods according to DS. We see that the presence of SC tends to dominate over other DSs in ConDS. Although ConDS presents more challenges than UniDS for LDD and UDS, there is almost no performance drop when moving from SC to SC+LDD or SC+UDS. Furthemore, for the DSs that include SC, the performance of most methods is inferior to that of foundation models even when it pre-trained, as shown in Figure
Takeaway 3 Generalization tends to be consistent across DSs. If method improves generalization for one DS, it tends to be effective for others. Namely, although models such as de-biasing, worst-case generalization, and domain generulization are designed to address specific DS (as detailed in Section their applicability is not confined to that particular shift.
5.3.1 Takeaways Common to both UniDS and ConDS
The following takeaways with UniDS have been previously identified by Sahoo 2024):
Wiles et al.
p.21p. We observe that these phenomena persist in the ConDS scenario.
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Takeaway 4 Heuristic augmentations and pre-training highly effective. As depicted in Figure heuristic augmentations overall improve the model's robustness across all DSs. This remains consistent when pre-trained weights are utilized except for ImageNet augmentation (Figure in the supplementary). All the augmentation methods even increase the performance of RosNet18 when used on Uniform, where there is no DS, establishing them as a more effective tool. Algorithms demonstrate improved performance with ImageNet pre-trained weights, as shown in Table 3 On LDD, UDS, and LDD+UDS most methods even outperform foundation models, as depicted in Figure
Takeaway 5 Generalization algorithms provide limited performance improvement. While some methods (L2D, PnD, OccamNets) perform well, most methods exhibit limitations. Existing gener- alization methods, though competitive in their original experimental setups and benchmarks, demonstrate reduced effectiveness in our more standardized and fair setup.
Takcaway Foundation pre-trained models are effective, but their performance can vary. Although foundation models demonstrate impressive performance on controlled datasets, they show limited performance compared to other baselines with ImageNet- preetriied weights, shown in Figure g Notably, they exhibit the lowest accuracy real-world datasets (Table 3 This observation underscores that the performance in image classification significantly depends on the datasets used to train these foundation mod- els, highlighting potential limitations in their applicability. F-g., for specialized datasets like Camelyon large models with zero-shot inference can barely make accurate predictions (Table 12 Furthermore, we find that their performances are significantly influenced by the prompt we input (Please refer Table 5 and Table 12 in the Section B.5 for more details).
6 Concluding Remark
Contribulion. In this paper, we introduce novel evaluation framework to understand the robustness of models against various distribution shifts, including UniDS and ConDS. Using this protocol, we can create distribution shifts in any mull--atribuute-nnnlaaed dataset, allowing for a more comprehensive understand- ing of robustness. Through our extensive evaluation involving 100K experiments, we find that ConDS present greater challenges compared to conventional UniDS, with spurious correlations being more prob- lematic than low data drift and unseen domain shifts. Our results indicate that heuristic augmentations and pre-training are effective tools for enhancing generalization, while more complex models offer limited benefits. Additionally, while large models are viable for image classification, their performance is effective only in specific scenarios and requires careful application.
Limitation and fulure work. While believe that our work makes promising step towards understanding how models behave under complex scenarios, there is still a lot more that can be done in this direction, we briefly discuss some of them. Our evaluation framework allows us to create controlled distribution shifts and assumes a uniform Lest distribution. Our framework also uses annolated, thus, interpretable altributes to create shifts. Using learned attributes would also be an interesting future direction. Furthermore, our study covers a limited range attributes, particularly in controlled real-world datasets such as CelebA and Deepin due insufficient samples for other attributes. Future research could explore the use of advanced controllable generative models (e.g., diffusion models) to address this limitation and cover broader range of conditions.
Acknowledgments. Myeongho Jeon, jointly affiliated with EPFL and Seoul National University, supported by the National Research Foundation of Korca (NRF) grant funded by the Korca government (MSIT) [RS-2024-00337693). Suhwan Choi was supported by the Ministry of Culture, Sports and Tourism & Korea Creative Content Agency [RS- 2024-00399433), and Artificial intelligence industrial convergence cluster development project funded by the Ministry of Science and ICT (MSIT, Korca) & Gwangju Mctropolitan City. We also extend our sincere thanks CRABs.ai for their generous financial support and the provision of GPU resources.
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A Detailed Experimental Setup
A.1 Setup for Controlled Distribution Shifts
We utilize multi-attribute datasets such as dSprites, Shapes3D, SmallNorb, CelebA, and DeepFashion to develop ConDS. These datasets enable us to sample images annotated with various attributes, showcasing range DSs from SC (SC, LDD, UDS). Additionally, we configure different attributes for each type of DS. For instance, in SC, pairs like (y", y²) and (y', y²) may exhibit spurious correlations. By covering all possible attribute combinations (with the attributes depicted in Figure 5) we ensure more comprehensive evaluation of scenarios.
For UniDS, we choose one attribute from three options, yielding three settings. In ConDS, we address two combinations: (SC, LDD), (SC, UDS), and (LDD, UDS). For each combination, one attribule is selected for the first DS and another for the second, following a 3C2 selection method. For the three-attribute combination ConDS, namely (SC, LDD, UDS), one attribute chosen for the first DS, another for the second, and the remaining for the last DS, in line with 3C3 approach. With five datasets in controlled setup, this generates total of 165 (source, target) pairs.
GOOL y object shape
y object shape
y category
OO
d object color
y object color
yo uzimuth
OL
g, background color
S background color
y lightening
OOO
:: object size
7 object size
y² clevation
DSPRITES
SHAPES3D
SMALLNORB
yf gender
y clothes category
dh hair color
d pattern
8 background color
y sleeve
object size
y fabric DEEPFASHION
CELEBA
Figure 5: Samples of controlled datasets. We provide visualizations of some samples along with their altribules.
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A.2 Dataset Configuration
We provide detailed counts for each split within the datasets. To create the DSs as outlined in Section 3 variations in dataset sizes across the DSs result from the limited availability of certain attribute combinations.
Table 6: Dataset size. Please note that we have included 1% counterexamples for SC in our input.
dSprites
Shapes8D
SmallNorb
CelebA
DeepFashion
UNIFORM
1,091
65
127
227
97
SC
1,091
127
227
97
LDD
1,080
624
1,600
224
112
UDS
1,080
192
500
224
96
SC +
LDD
1,091
158
324
227
99
SC UDS
1,091
49
101
227
101
LDD UDS
1,080
468
1,280
224
98
SC+ LDD UDS
1,091
119
259
227
92
TEST
810
1,280
3,125
800
80
Additionally, we detail the method used to introduce distribution shifts in the original datasets as follows:
dSprites: There are no predefined splits, such as train, validation, or test, in the dSprites dataset; contains only images and their attribute information. We first created train and test pools by randomly splitting the images, then sampled from each pool to introduce distribution shifts. For the object size altribute, we used the altribute values [0.8, 0.9, 1], labeling them small, medium, and large, respectively. Since the original dataset is grayscale, we applied our own colorization: Object colors include red (255, 0, 0), yellow (255, 255, 0), and blue (0, 0, 255), while background colors are set as orange (255, 153, 51), green (0, 153,0) 0), and purple (102, 0, 255). We allocated 20% of the training set as the validation set for parameter tuning, ensuring that both share the same distribution.
Shapes3D: There are no predefined splits, such as train, validation, or test, in the Shapes3D dataset; it contains only images and their attribute information. For the object size attribute, we used the attribute values [0.75, 0.964, 1.036, 1.179], labeling them as tiny, small, medium, and large, respectively. We used [0, 0.1, 0.3] for object color and [0, 0.1, 0.2, each unique combination of labels and attributes, we divided the data instances into training and testing sets. We allocated 20% of the training set the validation set for paramcter tuning, ensuring Ihat both share the same distribution.
for background color. For
SmallNorb: We used the original train-test split provided by SmallNorb, selecting clevations [0, 2, 4, 6, 8), azimuths [0, 8, 16, 24, 32], and lighting conditions [0, 1, 3,4, 4). To ensure consistency across categories,
manually adjusted the azimuth values for all animal categories, their initial starting points (0) differed. We allocated 20% of the training set as the validation set for parameter tuning, ensuring that both share the same distribution.
Using all samples in the dataset, we first selected the attributes Black Hoir, Smiling, and Straight, Hair. We then split each combination of these attributes into training and testing sets. The training set was used to create distribution shifts, while all test splits were combined to form a uniform distribution. These three attributes were chosen because they provide sufficient samples to accommodate distributiin shifts. We allocated 15% of the training set as the validation set for parameter tuning, ensuring that both share the same distribution.
CelebA:
DeepFashion: Using all samples in the dataset, first selected the attributes tezture {floral, solid}, shape ammin__ennttin no no_ _desss)ss, and style {chiffon, cotton]. We then split cach combination of these altributes into training and (esting sets. The training set was used to create distribution shifts, while all test splits were combined to form a uniform distribution. These three attributes were chosen bocause they provide sufficient samples to accommodate distribution shifts. We allocatod 20% of the training set as the validation set for parameter tuning, ensuring that both share the same distribution.
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A.3 Synthetic and Real-world Datasets
We can precisely manage distribution shilts with synthetic dalasets because they allow for complete control. Real-world datasets, on the other hand, offer the advantage of realism but may experience uncontrolled distribution shifts. For instance, suppose we create gender classifier with a dataset that exhibits a spurious correlation by sampling images older females and younger males. Despite our efforts, we cannot ensure uniformity across other attributes such as skin tone and gender. Given the advantages and disadvantages of each, we conducted experiments with a variety of synthetic datasets (Dsprites, Shapes3D, Smallnorb) and realistic datasets (CelebA, DeepFashion, iWildCam, fMoW, Camelyon17).
A.4 Benchmarking Methodology
We outline the benchmarking standards in Table
Table 7: Benchmarking Methodology
Aspect
Configuration
Justification
Evaluation Metric
Accuracy: For the coutrolled dataset, accuracy is used the evaluation metric since the test set is uniformly distributed, providing a reliable measure generalization. For iWildS datasets, accuracy fol- lows the sctup of the original paper.
Ensures consisteut generalization measure- ment and alignment with benchmark stan- dards.
Image Standardization
Pixel values normalized to range of (-1, 1).
Stabilizes learning and prevents gradient van- ishing.
Learning Stop Criterion
Early stopping applied with patience or a maximum of 10,000 iterations. Validation ac- curacy is measured every 100 iterations; the high- validation accuracy is not reached, patience de- creases by one. Upon achieving the highest valida- tion accuracy, patience resets to 20.
epochs
Mitigates overfitting while optimizing model performance.
Image Size
Configured per dataset: 64x64 (dsprites, shapes3d), 96x966 (smalinorb, camelyon17), 128x128 (deop- fashion), 224x224 (fmow, iwildcam), and 256x256 (celeba).
We maintain the original image sizes of the datasets.
A.5 Computing Resource
All our experiments were performed using NVIDIA H100 SOGB HBM3 GPUs and Intel(R) Xeon(R) Gold 6448Y.
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A.6 Implementation Details
To generale all the results reported in this script, fine-tuned the hyperparameters. For the controlled datasets, we adopt early stopping where training stops early once the patience limit is reached. Validation accuracy is measured every 100 itcrations and one patience is consumed the best validation accuracy does not improve. The specific values are detailed in Table S and Table 9 We conducted a grid search with these parameters to optimize results for each algorithm across all DSs and datasets.
Table 8: General Hyperparamcters.
Dataset
Learning Rate
Batch Size
Duration
Paticace
Optinizer
Controlled
[e22 le-3, Ie-4]
128
10000 iterations
20
Adam
Real world Ilo3, 56-4, 1o-4, 5o-5, lo-5]
128
Table 9: Hyperparameters for specific methods.
Method
Dataset
Hyperpmmmentrss
ViT
Model: ViT B 16
MLP
#layers: 4, hidden dim: 256
ImageNet
Controlled
seale lower bound: 0.08
Real-world
scale lower bound: 0.3
AugMix
Controlled
severity: 3, mixture width: 3
Real-wor worldw
severity: 7, mixture width: 5
RamdAug
Controlled
mum ope: 3, maguitude: 5
Real-world
ops: 2, mmguitude: 9
AntoAng
All
CIFARIO policy for dSprites, Shapes3D,, and SmalINorb.
Otherwise, ImageNet, policy
UBNet
All
base model patience: 10, base model training epochs: 50
PnD
All
ar: 0.2, og: 2, 0:: 4. g: 0.7, EMA,; 0.7,
base model patience: 10, base model training epoclis: 50
OccamNets
Acs: 0.1, Act 1, 700 3, ne 1, Tacoop
MMoW:
Tooco: 0.2
GroupDRO
All
A: 16-2
BPA
All
l: 8, m: 0.3, base model patience: 10, base model training epochs: 50 12
iWildcam
Touz: 15, 2, 72 1.0
ME-ADA
Tmaz: 15, 72 1, m.
SagNet
Andui 0.1
L2D
au 1, agt 1, 8: 0.1
IRM
All
aunealing ileratious: 500, A: (during ammealing), 100 (after anmesllnng)
CausRRI
Controlled
7: 0.5 (SmallNorb)) 0.3 (dSprites). 0.1 (DeepPashion, CelebA), (Shapes31))
Real-world
CLIP-lase
All
model: 'opemii/clip-vit pastccch222
CLIP-Large
model: "opemai//dfperri-larg--paaaai
InstruetBLIP
Inference
model: "Salesforce hwtp://ccbbbi--iiinnnn-te
beam: load Abit: True
top p: 0.9, repetition penalty: 1.5, length penalty: 1.0, temperature: max tokens: 20
Fine-tuning
lora-r:
L.V
Inference
model: "llava I1lvaaLf//llvv.1..55 7b- max new lokense 200
Fine-tuning
lomer: 128
Phi-J.5.vision
Inference
model: "micresoft, (tirrreaft/PPh-3.66
tokens: 200, temperature: 0.2
Fine-tuming
lom-r: 64
GPT--o mini
All
temperature:l, top,:1
GPT-1o
All
temperature: topp:1
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B Experimental Results
B.1 Comprehensive Results
Figure G displays the results for all algorithms trained from scratch across all DSs, while Figure 7 the outcomes for all algorithms trained using ImageNet pre-trained weights.
presents
ResMer:1
VIT
Aughtix
UBMet
Decastiota GrougORD
BPA
XE-ADA Baglet
2.00
CausIRL-X
R5aHe:50
MLP
Randkug
PwD
IRM
CauaTRI-C
1100
Traggnet:
Autohuu
DSPRITES
0.5
Accuney
0.2
0.3
UNIFORM
SC
LDD
UDS
LC+LDD
SC4
LDD UNS
SC-LDD UIS
SHAPES3D
1.5
0.5
Accuracc
ELS
0L4
0.9
UNIFORM
SC
LDD
UDS
SC+LDD
SC+UDS
UDD-UDS
SC-LDD-UDS
SMALL.NORB
0.5
Accuray
0.6
0.4
0.2
UNIFORM
SC
LDD
UDS
SC+LDD
SC+UDS
LDD-UDS UDS
SC-LDD-UIS
CELEBA
1.2
Accuracy
0.2
0.0
UNIFORM
80
LDD
UDS
SC+LDD
UC+
LDD uns
S0-LDDD- UIN
DEEPFASHION
1.5
0.5
Accuracc
022
UNIFORM
LDD
UDS
SC+LDD
SC+UDS
LDD-UDS
SC-LDD-UDS
Figure 6: Results for all algorithms from scratch. We plot the top-1 accuracy.
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BesSler18
Banding
PuD
BPA ADA
XE-ADA Sugfiet
120 IRM
CaueIRR-H -X CaunTRR,-
Imagenes AugNix
Autohug
111
USte:
GrangRan
DSPRITES
Accntmey
0.7
0.2
UNIFORM
SC
LDD
UDS
SC-LDD
SC+UDS
LDD+UDS
SC-LDD+UDD
SHAPES3D
0.2
0.3
UNIPORM
SC
LDD
LDS
SC-DDD
SCHUDS
LDDACDS
LDD UDS
SMALLNORB
1.2
Accerncy
0.4-
0.2
0.2
UNIFORM
SC
LOD
UDs
SC+LDD
SC+UTS
LDDACDS
CELEBA
1.3
Accoreey
0.2
0.2
UNIFORM
SC
LDD
UDS
SC+1DD
SC+UD8
LDD+UDS
SC-LDD+UDS
DEEPFASHION
1.2
0.5
Acctraity
UNROORM
LDD
LDS
SC-LDD
SC+UDS
LDD
SC-LDDUUUS
Figure 7: Results for all algorithms with pre-trained weight. We plot the 1 accuracy.
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B.2
Experimental Study Pre-training
Figure presents the aggregale results of pre-training across all algorithms and DSs.
Architecture
Augmentation
De-bias
Worst
SDG
OOD
Zero-shot
0.15
UNIFORM
SC
LDD
UDS
SC+LDD
SC+UDS
0.10
LDD+UDS
-0.15
SCDLDD+UDS
-0.20
Reutlet55
Beallet10)
VIT
Anghti
BandAug
fatoluu
Ubillet:
Pai
GroupOno
IPA
ADA
XE ADA
Sagflet:
1.20
Caue IRL
ConeTRL
CLIP -p-soss
CLIP-large
InstuctRLiP
LLaVA-15
Phi vission
GPT-4o
GPT-4o
Figure 8: Result with ImageNet pre-trained weight. The setup is the same as Figure
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B.3 Analysis across Different Data Sizes
We further analyze the aggregate results across different dalaset sizes. As shown in Table 10 the algorithms in three variations: small, medium, and large (see from Figure 9 to Figure 20 Although the overall trend remains similar, some differences are observed.
evaluate
Table 10: Various Dataset size. Please note that we have included 1% counterexamples for SC in our input.
Shapes3D
CelebA
small
middle
big
small
middle
big
130
194
340
LDD
624
1,248
1,872
112
224
336
UDS
192
384
576
112
224
336
SC + LDD
158
316
473
114
227
340
SC + UDS
49
97
146
227
340
LDD + UDS
936
1,404
SC+ LDD + UDS
119
237
355
114
227
340
Architecture
Augmentation
De-bias
Worst
SDG
0OD
Zero-shot
0.4
UDS
SC+LDD
SC+UDS
LDD+UDS
LDD
0.2
Reatlet50
VIT
InageMot
Aaghtt
RandAng
Autiokng
Ubilee:
PnD
Occanletts
Gevuptño
BPA
ADA
AbA
Saglet
1,20
TRN
CauaTRI-N
CaliIRL-C
CLiP-base
CLIP-large
InstroctLLiP
LLaVA-1.5
Phi-3 5-ision
GPT 4G
Rectee
GPT-4o
Figure 9: Scratch Shapes3D result with small dataset size.
The setup is the same as Figure
Architecture
Augmentation
De-bias
Worst
SDG
0OD
Zero-shot
UDS
0.2
SC+LDD
0.0
SC
LDD UDD
002
SC+LDD+UDS
RosNet50
Reatetio1
VST
Inagalee
Anggeax
Randkag
Aurokug
VENee:
PaD
Occanilets
Grougono
BРA
ADA
ME- ADA
Sagles
1,20
TRM
GausTRL -
CLIP-base-
CLIP-large
InstructBLIP
LL-V--15 1.5
Phi- S-vision
GPT-4o-mini
GPT 4o
Figure 10: Scratch Shapes3D result with middle dataset size. The setup is the same as Figure 3
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Architecture Augmentation
De-bias
Worst
SDG
0OD
Zero-shot
UDS
SC+LDD
0.0
SC+UDS
UUDS
-0.
L
Reatlet30
Reallec10)
VST
InageMet.
Aaghht
RandAng
Kuttokagg
PaD
Occanlete
Group080
BPA
ADA
ADX
Saglett
1,20
TRM
GaweTRl -N
CauxIRI-C
CLiP-base
CLIP-large
InstructBLIP
LLiVA-1.
Phi-3 5-vision
GPT 4a
GPT
Figure 11: Scratch Shapes3d result with big dataset size.
The setup is the same as Figure
Architacture
Augmentation
De-bias
Worst
SDG
00D
Zero-shot
SC
0.2
LDD
0.1
SC+LDD
SCHUDS
0.1
LDD+UDS
0.2
SC+LDD+UDS
Reaket
Raaketa0
Reatlet101
VIT
Inagalet
Augiti
Randeag
Autoking
UBilee
PaD
Occanilets
GroupD80
BPA
ADA
NE ADA
Baglet
L20
IRM
OousTRL -M
Ckue IillIt
CLiP-base
CLIP-lerg PPP-lagee
InstructBLIP
LL.VA-1.5
Phi Phi-3 5-vision
GPT-4o mini
GPT 40
Figure 12: Pretrain Shapes3d result with small dataset size.
The setup the same as Figure
Augmentation
LDD
0.1
UDS
0.0
SC+LDD
SC+UDS
0.1
LDD+UDS
002
UCH
0.3
Beahett.0
Realeec
VIT
Inagetiee
AaghLx
RandAug
AntooAug
tiMee
PrD
Decanlette
GroupOna
8PA
ADA
ME: ADA
Sagllet
1,20
TRM
CauaTRo NITRR--0 -X
CaueIRl-
CLiP-base
CLIP-large
InstruciBLIP
LL-A-115
Phi-355vision 5-iison
GPT 4o
GPT 40
Figure 13: Pretrain Shapes3d result with middle dataset size.
The setup is the same as Figure
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Architecture
Augmentation
De-bias
Worst
SDG
0OD
Zero-shot
0.1
LDD
UDS
LDD
-0.1
UCD
UDS
SC+LDD+UUD
Basket50
Beatlet:01
VIT
Inagaties
Aughtx
RandAag
AusoAag
VBllles
PnD
Occazilete
GrouptRO
8PA
ADM
ADA
Saglies
1,20
1RH
Caus IRt--)
CausIRL
CLIP-bae
CLIP-lange
InstruciBLit
LLMV-1155
Phi- Phi-3.5 3-vison
GPT 4o
GPT-4o
Figure 14: Pretrain Shapes3d result with big dataset size. The setup is the same as Figure
Architecture
Augmentation
De. bias
Worst
SDG
0OD
Scro-shot
0.20
0.15
0.10
0.05
SC+LDD
SC+UDS
-0.05
LDD+UDS
00.10
UCDLDD+UDS
-0.15
Reables
Reakee500
VST
Imagollet
Angela
BandAag
Autollg
Usilet
Pabl
Occant
Group030
BPA
AD4
NE ADA
Saglet
L.00
IRN
CeusIRt-N
CausTRL-C
CLiP-base
Pargg
InstrctBBB
LLaVA-1.
Phi 5.5.5.00 visioon
GPT-4o
Beatle
GPT
Figure Scratch CelebA result with small dataset size. The setup the same as Figure
Architecture
Augmentation
De bias
Worst
SDG
0OD
Zero shot
SC
LDD
UDS
0.05
SC+LDD
0.00
0.05
LDD+UDS
UDD
0.15
Reelee50
Beafeet101
VIT
Randaug
lutoluu
UBllic
PmD
GrouptRd
DPA
ADA
ME ADA
Sagllet
L.2
ITM
Caue IRL-X
CaueTRL-O
CLIP CLIP-base -aoe
CLiP-large
InstructBLIPP
LLaVA-1.
Phi- S-vision
GPT.4o
GPT
Figure 16: Scratch CelebA result with middle dataset size. The setup is the same as Figure 53
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Architecture
Augmentation
De-bias
Worst
SDG
0OD
Zero-shot
0.2
LDD
0.1
UDS
SC
00.1
SC+UDS
0.2
LDD+UDS
-0.3
Beatle 150
RecMett01
V1T
Inagatiat:
Anghix
RandAug
Autooug
PnD
Occanletts
GeoupORo
8PA
ADA
ME- ADA
Sagliet
1,20
TRN
Caua -N
CausIBL-O
CLIP-base
CLIP-large
InstructBLIP
LLMA-115
Phi-3.5-viiio
GPT 40
GPT-4o
Figure 17: Scratch CelebA result with big dataset size. The setup is the same as Figure
Architecture
Augmentation
Do-bias
Worst
SDG
00D
Zero-shat
SC
0.05
LDD
0.00
SC+LDD
00.05
SC+
LDD+UDS
-0.10
SC-LDD+UDS
Reutee
VIT
Aughix
Bandkug
Autrolug
Ubilet
PnD
Occanl.esa
Grouptnn
IPA
AD4
NE ADA
Saglet
1,00
INN
Cane IRI-RUL- -
CauaTRL-C
CLIP-bba base
CLiP-large
InstautBBBB
LLaVA-1.5
Phi Phi.3 Svision
GPT-4o
Bealle
GPT
Figure 18: Pretrain CelebA result with small dataset size. The setup is the same as Figure
Architecture
Augmentation
De-bias
Worst
SDG
OOD
Zero-shot
LDD
0.1
UDS
SC+LDD
0.2
SC-UDS
LDD+UDS
SC+LDD+UDS
Resteec0
Reaket101
V1T
Inageliet
Augeti
RandAug
Autoouug
UBNee
Pab
Occanliets
GrouptRO
BPA
ADA
ME: ADA
1,20
CauaTRt--X
CeueIRL-C
CLIP-base
CLiP-large
InstructBLit
LL-VA-1.5
Phi-3 Phii335-wission S-vision
GPT.4o 4o
GPT
Figure 19: Pretrain CelebA result with middle dataset size.
The setup is the same Figure 3
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Architecture
Augmentation
Do-bias
Worst
SDG
00D
Zero-shat
SC
LDD
0.02
SC+LDD
0.00
SC+
0..0
LDD+UDS
0.04
SC-LDD+UDS
00.0
Reutee
Beallet
VIT
Aughix
Bandkug
Autrolug
Ubilet
PnD
Occanl.eca
Grouptn0
IPA
AD4
NE ADA
Saglet
1,0
INN
Cane WinIRRL- N
CauaTRL-C
CLIP-bbs base
CLiP-large
InstactBBBB
LLaVA-1.5
Phi Phi.3 5ivision
GPT-4o
Figure 20: Pretrain CelebA result with big dataset size. The setup is the Figure
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B.4 Results for Real-world datasets
Table presents the detailed results for each dataset listed in Table
Table 11: Experimental results real-world datasets.
The setting is exactly the Table
iWildCam
nMoW
Camelyon17
Scratok
Pre-traininng
Scratch
Pre-training
Seratch
Pre-trušning
18
51.77(++0.7)
+0.43))
27. 27.13(
41.08(
82.53( +0.25
(4.10(+
ReaNet50
(3.35()0:34)
66.75[20.0) 39)
28.35
19.75(10.32)
75.85(110.3)
86.78(20.38)
Architecture
ResNet101
43.41(=:0077)
09.71[=0.20
22.35 (0000)
00.17)
+0.37)
81.21(=0.199
ViT
51.08(+00333)
00.82[-0.45)
23.20( +0033)
51.74( +0.46)
76.781 +0.188
01.27(++0344)
avg.
30.05
66.45 2.42)
25.51 (..1.50)
47.80(1.2.35
77.68 74)
86.65 (1.165))
Imagenet
48.21(+0.211
64.17(-00. 0-339)
26. 10(+-))33
47.32( 00.33)
72.08(+00.38 (0034))
87.9 87.96(+0.30)
Augmentation
AngMix
51.90(10:29))
65.61[10.05
29.94(1.:27))
10.95(10.22
74.13(10.41
78.72
RandAug
47.19(=20 28)
65. 06.14[00.03
31.12(
19.18( 48)
7T.21( +0.00000000 11)
90.21(=0.00 10)
AutoAug
61.57(++0322)
70. 10.19000000)
31.64 (+0.44)
50.13( +0.24)
73.23( +0.29)
90 00.56(+0.477
avg.
19.79(111.13)
(6.36)
29. 71 (..))84)
49.291 0..65)
14.16(11.10))
89. 19.77 0.662)
UBNet
32.96(=+0117)
57. 57.96(=0.39)
16.30(
37 17.066(0.551)
13.77(=0038))
82 82.06(=0.122
De-blasing
PnD
45.25(+0042)
61.03(=0048)
21.01(++054)
40.20( (20(+0.00 +0.40)
83.8 83.81(++
79.60( (0.00(+0.22
OceamNets
53.67(11040)
10.45)
29.06(1.0.22 (.0.27)
10.39)
74.97( 10.32)
11.77(11.47))
aug.
63.96(=)601z)
68.10 2.7)
2
41.39( 78)
77.12
81.17 #1.17(= 75
GrouDROO
45.15(110.2))
63 15(1...223 0.23))
25.98( (.0.46)
49.07( 10.44)
73.37 10.38)
80.90 10.35)
Worst-onse
BPA
35.54(20:22)
26.06(20.18
3.71(10.26)
10.17(10.17)
76.99(10.277
20.08(
aug.
40.85(++4.88)
(9.60
17.80 64)
2.(
18 1.81)
88.08(
ADA
48.47(++0.11)
00.39[=0.43)
29.71(+003000
+0.40(+00441)
80.451+00
88.74(
ME-ADA
52.62 26)
65.87(=0.10 10)
28. (63)))333
51. 11.43(00.355
80 29)29(20) 19)
81 $1.93(
SDG
SogNet.
54.07(++0.50
00.83|
20.53(++0.00)
49.32( +0.32)
78.02( +0.30)
31.10[+0.44
L2D
47.78(110.16)
64.09 .0.49)
23.90(
45.65( 10.34)
86.32 10.23)
94.85(110277)
ang.
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(7.44
27.90 (=:.06))).)
49. 19.20(+1.86)
81.42( 1.68)
(.
34.55(+00.11)
57.36(+00
11.40(+0.55)
30.88( +0.36)
72.47(++0335)
85.45(
00D
CamIRL-M
(....5))
68.34[ 1...4.00445
22.32 (10.28)
49.44( 10.11)
72.10 10.26)
(0..3(
Caus|RL-C
53.36(200. 10)
65. (0.366/0011
28 61 (=0) 18)
19. 00.88320.000 20.0000113 13)
81.16 10.116/20000 (0)
88 98.
ang.
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63 23369[ (..669(+)8)))))
80. 78 (55088))
49.98(+
(+:2.00)
84. 78.
CLiP-base
13.97
13.30
50..11
CLIP-large
13.70
23.46
00.01
BLII
1.86
18.17
68.03
VL-15
4.61
16.32
51.09
Zero-shot
Phi-3.5-vislon
9.91
10.88
66.
GPT-lo mini
43.36
19.89
64.98
GPT-4o
51.80
25.58
19.59
18.
58.43
29

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Published in Transactions on Machine Learning Research (01/2025)
B.5
Further Analysis Zero-shot Inference
To utilize foundation models for image classification, employed various prompls to extract labels from the input images. Table 12 and Table 13 detail the prompts used in our prompt engineering approach.
Table 12: Prompts used in Zero-shot inference.
Foundation Model
Prompt Type
Prompt
CLIP
General
"a photo of a label"
General,
Classify the image into lobel1.... tabelc. Please provide only the name the label.
Choose label that best describes the imagge. Here the list of labels to choose from: label labele.
Generaly
Please provide only the name of the label.
dSprites
Shapee3D
Classify the object in the image into lobely...., or iabele. Please provide only the name of the label.
SmallNorb
LLaVA-1.5
CelebA
Classify the person in the image into mole or female. Please provide only the name of the label. a person westring dress or not? Please amswer in yes or no.
InsIrutBLIP
DeepFushion
Pli-s.5-vision
Classily the object unimal in the image. Here is the list of labels to chucee from: lubel, labelg-
iWildcam
GPT-4o mini
Please provide only the name of the label.
GPT-4o
Classify the building or land- use in the image into label,..., labele.
IMoW
Please provide only the name of the label.
Camelyon17
Please auswer yes if the image contains any tumor tissue, and no otherwise.
General;
Please provide only the name of the label.
Camelyon17
Please auswer yes if the image contaius any tumoor Lissue, and no otherwise.
Generaly
Please respond with single word.
Camelyon17
Please aualyze the innge and determine if it contains any tumor tissue.
Tailored
Respond with 'Yes' tumor tissue is present, or 'No' is
Table 13: Prompt formats for vision-language models.
LL 2.5 IestractS1
Pli 3.5 asson
GPT 40, GPT to somi
Prompt Formal
USRR: image 2o1 prompt 'nn ASSISTANT:
USFR: < jinege 1| > prompt
prompt
B.6 Fine-tuned Open Source Foundation Model
We observe in Table 3 that the zero-shot performance of foundation models is constrained when applied to real-world datasets. Our evaluation on real-world datasets like Camelyon17, which contains complex cell images, iWildCam with its camera trap images of diverse animal species, and FMoW's satellite images present unique challenges due to their niche content and visual complexity. This high level of domain specificity, with features likely outside the foundation models' general scope, limits their capacity to generalize effectively, particularly in zero-shot settings.
An intuitive approach to evaluate this is by fine-tuning the vision encoder these specialized datasets. Through fine-tuning with LoRA Hu et al. (2022). we found that the foundation models performed as expected, showing high effectiveness for these datasets. The results are presented in Table 14
Table 14: Fine-tuned open source foundation model. w and w/o denote with and without fine-tuning, respectively.
iWildCam
Camelyon17
FMaW
w/o
w/o
w
w/o w
LLaVA-15
4.64
91.12
51.09
95.32
16.32
72.67
P.5.3.5. Vision
Vission
9.91
91.19
66.71
93.35
10.88
77.02
InsIructBLIP
1.86
12.13
68.03
99.87
18.17
41.18
30

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Published in Transactions on Machine Learning Research (01/2025)
B.7 Visualization Invariant Feature Learning.
We investigate feature invariance with respect to labels and attributes using the CelebA dataset. CMAP (Mclnnes et 2018 utilized for visualization. Figure 21 illustrates the feature space of the best and worst-performing algorithms, while Figure 22 compares learning from scratch with pre-training. While all the algorithms demonst rate invariance to LDD and UDS, ViT exhibits sensitivity to SC. In contrast, both Carge and Image.Net remain invariant to all DSs. In Figure 22 the VIT model with pre-training exhibits better invariance to attributes compared to the ViT model trained from scratch.
fessale
block
smiling
CLIP-Large
straight
hair
femalle
black hair
smiling
elose
ImageNet
augusentatin
striiggtt
male
else
etse
female
hair
black
sauling
male
olse
clse
hair
straight
Figure 21: Visualization invariant feature learning. We visualize the top two algorithms, CLIP- Large and ImageNet, along with the worst-performing algorithm. In this sctup, 'black hair' is used to create SC, 'smiling' is used to generate LDD, and 'straight. hair" is used to produce UDS.
female
block
smiling
clso
VIT
pre-training
mole
else
else
straight
fenale
block hair
smiling
ViT
seratch
male
clue
else
stright
haar
Figure 22: Scratch Pre-training. The setup is exactly the same Figure 21
31

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Published in Transactions on Machine Learning Research (01/2025)
B.8 Generation of Attributes with Augmentations
Our framework requires datasets with rich attribute annotations lo create ConDS. However, such dalasets are limited as annotations are expensive. We did consider using augmentations to create additional attribu- tions, but augmentation tochniques algorithm basclines might directly address these shifts in this sctting. However, for the rebuttal, we applied three types of corruptions from ImageNet-C (Hendrycks & Dietterich, noise, snow, and elastic transform on CelebA. Each attribute was evaluated under two conditions (corrupted and uncorrupted). Figures 23 and 24 exhibit the evaluation results with this setup.
Architecture
Augmentation
De-bias
SDG
0OD
Zero-shot
0.5
LDD
0.4
UDS
0.3
SC+LDD
0.2
SC+UDS
0.1
LDD+UDS
0.0
SCHLDD4UDS
Reslet18
ReaNecão
Reghett01
Vi2
Inngoliet
Aughix
BandAug
Autolagg
Uilket.
PaiD
DecanBeete
GroupDR0
BPA
ADA
ME ADA
Saglet
1.20
IBM
CausIBL-H
CLiP-base
CLIP large
InstructBLIP
LlaVA-1.5
Phi-3, 5-vision
GPT 4o mini
GPT-40
Figure 23: Scratch CelebA result with attributes generated from augmentation. The setup is the
same as Figure
Architecture
Augmentation
De-bias
Worst
SDG
00D
Zero-shot
SC
0.1
LDD
0.2
SCHLDD
SC.UDS
0.1
LDD UDS
SC+LDD+UDS
0.1
BasMert50
Reatiet:01
VST
Inagelet
Aughta
Randkag
Autooing
UBilet
PaD
Occasilets
GrouptRo
BPA
ADA
ME- ADA
Sagles
1.00
TRM
GeusTRE-XX
CausIRL-- IRL-C
CLiP-base
CLIP-largs large
InstructBLIP
LLaVA-1.5
Phi-3. S-vision
GPT-4o-mini
GPT 4o
Figure 24: Pretrain CelebA result with attributes generated from augmentation. The setup the
same as Figure
32

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Published in Transactions on Machine Learning Research (01/2025)
B.9 Results for Distribution Shift Generated by Clustering
We leverage DINOv2 (Oquab el al. 2024 to extract rich image features, using the resulting feature clusters as proxies for DSs. The motivation behind this approach aligns with that of Section B.8 namely, the necessity for a scalable method to simulate DSs without depending on costly and labor-intensive attributo annotations. As displayed in Table 1 we group image features into six clusters based on their embeddings from DINOv2 and treat. these clusters as distinct distributions. Figures 25 and 26 exhibit the evaluation results with this setup.
Table 15: Cluster groups. From six clusters, we create four distribution shift groups and one test group, simulating different environmental or contextual variations.
Group
Group 2
Group 3
Group
Test
Cluster
1, 4
2, 4
1,
2, 5
3, 6
Architecture
Augmentation
De-bias
Worst
SDG
00D
Group
0.2
Graup
0.0
Group
-0.2
Group
Neallet18
Rocllet50
Bealle 101
V1T
Inagelet
Aughix
Bandkag
Autofug
UBliiet
PnD
Dccannleta
oroupORU
RPA
ADA
NE AD8
Saglett
1,20
IRM
CanaIRL.
Cana
Figure 25: Scratch CelebA results cluster groups.
The setup the Figure
Architecture
Augmentation
De-bias
Worst
SDG
00D
Group
-0.1
Graup
0.2
Group
0.3
Group
0.4
Reallet18
Reailet50
Beatte Peattet100 101
VET
Inagelles
Aughtix
Bandkog
Muto Augg
UE3ee
Occanlleta
Groupord
8РA
ADA
ADA
Sag
IRM
CausIRL-O
Figure 26: Pretrain CelebA result across cluster groups. The setup is the same as Figure
33

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Published in Transactions on Machine Learning Research (01/2025)
B.10 Performance Sensitivity for Hyperparameters
We evaluate the performance ivvsitiiiy of the algorithms across all datasets and compule the average results.
For detailed hyperparameter configurations, please refer to Table g
Figures 27
40 illustrate the results.
SC
SC
LDD
LDD
-1
-
UDS
UDS
2
SC+LDD
SC+LDD
3
SC+UDS
SC+UDS
LDD+UDE
SC+LDD+UDS
SC+LDD+UDS
0.08
0.3
0.7
0.08
0.3
0.7
scale lower bound
scale lower bound
ImageNet (Seratch)
Results of ImnggeNe (Pretrained) controlled datasets
iWildCam
iWildCam
10
-2
IMoW
fMoW
Camclyon17
20
Camelyou17
0.08
0.3
0.7
0.08
0.3
0.7
scale lower bound
scale lower bound
2) Results of ImageNet (Scratch) on real-world dalasets
ImageNet (Pretrained) real-worid
Figure 27: Hyperparameter Exploration for ImageNet.
34

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Published in Transactions on Machine Learning Research (01/2025)
SC-UDS
SC+LDD+UDS
-2
-5.0
0133
6.
0.9
0.1
03
0.9
0.1
0.3
0.0
0.1
0.3
0.0
0.1
0.3
0.9
0.1
0.1
0.9
0.1
0.3
0.9
mistuue width
micture
width
Rauits of Aughis
SC
UDS
SC-LDD
LDD+UDS
SC+LDD-UDS
sevesity
-2
-2
-2
5,0
0.1
0.3
0.0
0.1
0.3
0.9
0.1
0.
0.9
0,1
03
0.9
0.1
03
0.0
0.1
0.3
0.0
0.1
0.3
0.0
misture wädta
mixture
width
mixture width
Roulta Augiix (Pestainedd controlied
SWildCCmm
IMW
Camelyon1
(WildCeem
FMoW
Camedyor
0n
05
110
-6
-15
Resuls of Aughis (Scnach) ueal seal-world
Resalts Aughiix (Pretraned) reiloweeld datasees
Figure 28: Hyperparamcter Exploration for AugMix.
SC
LDD
UDS
SCELDD
SCI
LDD: &
SC|LDD UDS
11.0
225
-5
-2.5
-5
10
3
5
3
7
mooguitdde
magnitude
maguiidde
maggittace
magzirude
maggiitdee
magnitudo
Reauls RandAuu contolled datsecs
SC
LDD
SC+LDD
SC++DDS
LDD+UDS
SC+LDD+UDS
0.0
-25
-
3
3
Resuts (Prctaised) controlled
(WildCCa
EMoW
sWildCCm
BMoW
Camelgon12
-2
unguitude
Itagaltuee
Boulls (Scuadi rell-wncl
Reauls (Poctaised) ceal-worid datsets
Figure 29: Hyperparameter Exploration for RandAug.
35

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Published in Transactions on Machine Learning Research (01/2025)
0.00
SC
1):25
-0.5
LDD
LDD
0.50
-1.
UDS
0.75
UDS
SC+LDD
1..0
SC+LDD
-15
1.25
SC+UDS
SC+UDS
2.0
1.50
LDD-UDS
LDD+UDS
2.5
1.75
SC+LDD+UDS
SC+LDD+UDS
2.00
3.0
5
10
20
10
20
base model patience
base model patience
Results of UBNes (Seratch) controlled datasets
b) Resulls UBNtt (Pretnined)) on controlled dateses
0.0
iWildCam
iWikICam
IMoW
2
fMoW
01.5
Canclyon17
Camelyyn11
110
5
10
20
10
20
base model patience
base model patience
UBBNa real-word
Results UBNet (Pretrained) real-world datasets
Figure 30: Hyperparameter Exploration for UBNet.
36

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Published in Transactions on Machine Learning Research (01/2025)
SC
LDD
UDS
SC+LDD
SC+-UD
LDD HUDS
SCD-LDD UDS
6.0
-2
0.5
0.3
0.5
0.5
1
0.5
2
0.5
0.5
of Ousameite contelledd
SC
LDD
UDS
SC+LDD
SC-+UDS
LDD+UDS
SC+LDD+UDS
-2
0.55
0U5
0.5
OU5
2
01.5
0.5
MfvelieeOOOessNNiii (Phosincd) cotinillal
(WildCam
Camelyun17
[WildCCa
Camelyun17
10
-10
10
20
(Promsincd)
Figure 31: Hyperparameter Exploration for OccamNets.
37

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Published in Transactions on Machine Learning Research (01/2025)
-000
-0.2
LDD
LDD
0.04
-0.4
UDS
UDS
-0.06
SC-LDD
SC+LDD
-0.6
-0.08
SC-UDS
SC+UDS
-0.8
LDD+UDS
-0.10
LDD+UDS
SC+LDD-UDS
-0.12
SC+LDD+UDS
0.1
0.01
0.001
0.1
0.01
0.001
X
Results of GroupDRO (Seratch) controlled datasets
b) Results of GroupDRO (Pretrained) on conirolled dalaneis
iWildCam
iWildCam
-5
fMoW
fMoW
10
Camelyon117
10
Camelyou17
0.1
0.01
0.001
0.1
0.01
0.001
GroupDRO
Results GroupDRO (Pretrained) real-wocld datascts
Figure 32: Hyperparameter Exploration for groupDRO.
38

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Published in Transactions on Machine Learning Research (01/2025)
SC
LDD
UDS
SC+LDD
SC-UDS
LDD+UDS
SC+LDD+UDS
-2
-2
0.1
03
0.0
0.1
033
0.9
0.1
0.3
0.9
0.1
0.3
0.9
0.1
0.5
0.9
0.1
0.5
0.
0.1
0.5
09
Reaals BBA contolledd cheanct
LDD
UDS
SC+LDD
SC-UDS
LDD+UDS
SC+LDD+UDS
-5
-2
0.3
0.3
0.1
0.3
0.9
0.1
0.3
0.9
0.5
0.1
0.3
0.1
0.3
0.9
(Premamed)
iWil-CCam
Camelyon17
Camelyon
-5.
-7.5
-1
011
03
69
0.3
0,1
0.3
0.0
0.3
0.9
0.1
0.3
0.
0.1
0.3
0.5
Reealll (Senach) ral-wurki datrack
(Pretninnd) reak-werid denects
Figure 33: Hyperparameter Exploration for BPA.
SC
LDD
CDS
SC+-LDD
SC-UDS
LDD+UUDS
LC
-5
-1
5
-10
-10
10
15
20
10
15
20
10
15
20
10
15
20
10
15
20
15
20
10
16
20
Touz
Tear
Tras
Resultu of consolled durasets
LDD
UDS
SC+LDD
SC-UDS
LDD+UDS
SC+LDD+UDS
-
-5
10
10
1G
20
15
10
15
20
10
20
10
15
15
10
15
20
Tixer
Texr
Tear
Trxaz
ormmllial
BMlo
Commlyon17
Camndyon onl
22
-10
-31
6.0
-1.0
-20
1.5
-30
10
15
20
10
15 Ten.
20
20
10
10
15
20
Tewe
Teme
T
Te
T...
(Scuuch) on
oDADA (Pretraine))
Figure 34: Hyperparameter Exploration for ADA.
39

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Published in Transactions on Machine Learning Research (01/2025)
LDD
UDS
SC-LDD
SC++UDS
LDD+UDS
SC+LDD-UDS
0.0
-25
20
-000
20
20
10
15
10
15
20
10
15
20
15
20
10
20
10
15
20
10
15
20
Basils
anttnilial
SC+LDD
LDD+UDS
SC+LDD-UDS
-10
-10
10
200
20
10
15
10
20
10
15
20
15
10
20
10
15
16
20
Texer
Tiver
Tine
Twae
contelleed
(WildCam
Canelyou17
[WildCom
Camelyun17
00
0.0
-05
-23
--0
20
-30
T
7/
Teuc
Tix
ME-ADA nadlwarti
Figure 35: Hyperparameter Exploration for ME-ADA.
40

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Published in Transactions on Machine Learning Research (01/2025)
11
LDD
LDD
-2
2
UDS
UDS
3
3
SC+LDD
SC+LDD
--4
SC+UDS
SC+UDS
LDD-UDS
LDD+UDS
7
SC+LDD+UDS
SC+LDD+UDS
0.01
0.1
0.01
0.1
Ande
Aedo
Results SagNet (Seratch) controlled datasets
b) Results of SugNet (Pretrained) on conirolled dalasets
iWildCam
iWildCam
iMoW
fMoW
10
Camclyon11
Camelyou17
0.1
10
0.1
10
Audte
Ande
SugNet (Scratch) on real-world
Results of SagNet (Pretrained) real-world datasets
Figure 36: Hyperparameter Exploration for SagNet.
41

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Published in Transactions on Machine Learning Research (01/2025)
SC
LDD
UDS
SC+LDD
SC-UDS
LDD+UDS
SC+
-2
11.1
10
0.1
10
0.1
10
11.3
10
DL1
0.1
10
11.1
10
(Serneh))
SC+LDD
SC-UUDS
LDD+UDS
SC-LDD
--2
2
-2
P2
by (Pestrinedd consolled datzacts
:WildCam
.MoW
Camclpom17
iWL&CCam
IMoo
Comelpon17
-10
-
-15
0.1
0.1
10
[1]
10
0.
10
0.1
0.1
10
Resuts 122 (Scrnch) real-woecd datssess
Resuls of L20 (Pretrined) real-world dotnects
Figure 37: Hyperparameter Exploration for L2D.
SC
SC-LDD
SC+UDS
LDD+UDS
SC+LDD-UDS
-25
22
0.5
200
250
500
1600
250
500
1000 Irecutions
250
500
1000
250
500
1000
250
5.0
1000
500
10:0
250
500
1000
annotling Itcections
anncoling Exccutions
anncoling
Rouls of (Scmtc) dassets
SC
SC-LDD
SC+UDS
LDD+UDS
SC+LDD-UDS
2.5
3,9
250
500
10000
250
500
1000
250
500
1000
250
500
1000
250
5:0
1000 Ieerations
250
500
10.0
250
500
1000
Itcestions
anncaling
casealing
Rouds (Prininal)
[WildCCa
Cumndyan17
Camdlyuu77
-10
-00
-13
10
250
attandingg
arneding Hoenskine
atrsoding
atraccling lnmiiiis
atrssding tremsius
Reauls [RM (enusd) real-woeld
Resalts (Poctaiscdi soal-word drasce
Figure 38: Hyperparameter Exploration for IRM.
42

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Published in Transactions on Machine Learning Research (01/2025)
SC
-5
10
LDD
LDD
10
UDS
UDS
20
15
SC+LDD
SC+LDD
30
20
SC+UDS
SC+UDS
25
LDD-UDS
LDD+UDS
30
50
SC+LDD+UDS
SC+LDD+UDS
0.1
10
0.1
10
Results CausRLL-M (Scratch) controlled datasets
b) Resulls of CausRL-MM (Pretrained) costrolled dalascts
iWildCam
iWildCam
fMoW
-10
[MoW
-20
Camelyon17
Camelyon17
40
20
0.1
10
10
7
CausRRL-M on real-world
Results of CaudRRLM (Pretrained) real-world datasets
Figure 39: Hyperparameter Exploration for CausIRL-M.
43

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Published in Transactions on Machine Learning Research (01/2025)
LDD
LDD
2
UDS
UDS
3
SC+LDD
SC+LDD
SC+UDS
SC+UDS
-5
LDD-UDS
LDD+UDS
SC+LDD+UDS
SC+LDD+UDS
0.1
10
0.1
10
Results of CausRLL-C (Scratch) costrolled datasets
b) Results of CasIRLL-C (Protrainad) on controlled dalascts
0.0
0
iWildCam
iWildCam
2.5
5
IMoW
IMoW
5.0
Camelyon17
7.5
Camelyon17
0.1
10
0.1
CausRLL-C
Results of CaustRL-CC (Protrained) real-world datasets
Figure 40: Hyperparameter Exploration for CausIRL-C.
44

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Published in Transactions on Machine Learning Research (01/2025)
B.11
Computaional Cost
We provide the time and memory costs for all the algorithms, using iWildCam as the dataset for this analysis. Table [6] shows the results.
Category
Method
Time (seconds per epoch)
Memory (MiB)
ResNet18
1275
3274
ResNet50
1270
11640
Architecture
10
1289
17179
ViT
1258
17844
ImageNet
1405
11640
AugMix
1617
11640
Augmentation
RandAug
1418
11640
AutoAug
1357
11640
ADA
13011
11487
ME-ADA
1305
11487
SDG
SagNet
1281
11718
L2D
1607
24393
IRM
1272
11640
OOD
CMDDDRL-MMD
1275
11563
Ca-RRRI-CORAL.
1286
11606
UBNet
1262
11489
bias
PnD
1658
62225
OccamNets
3218
14669
GroupDRO
1258
11562
Worst-case
BPA
1249
12235
InstructBLIP
50
70567
LVLM (LoRA)
LLaVA-1.5
308
110880*
Phi-3.5-vision
577
12623
Table 16: Performance comparison of different methods across categories, showing time (in seconds) and memory usage (in MiB). Training was performed with a batch size of 128 on single H100 GPU. LVLM models fine-tuned using LoRA for Table 14 For LLaVA-1.5, due memory constraints single H100, multi-GPU training was used with two H100s, each handling a batch size of 64, and the total memory usage was combined.
45


Reviews:
[{"review_id": "yp1JiFoZVv", "reviewer": "Reviewer_6Dtq", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "### Strengths\n\n1. The proposed ConDS evaluation framework offers a more realistic method to evaluating model robustness against the distribution shifts. By considering multiple domain shift attributes, the paper provides important insights that help the machine learning community better understand the domain generalization problems and how to handle them more effectively. \n\n2. The evaluation includes a broad set of 26 algorithms and 168 source-target pairs across multiple datasets, offering a solid analysis of various types of domain shifts and how they affect the model generalization in general.\n\n3. The paper provides a detailed analysis of the limitations of vision-language foundation models (e.g., CLIP and InstructBLIP) when applied to complex distribution shifts, contributing practical values to the community.\n\n4. The paper is well-written with clear motivations.\n\n### Weaknesses\n\n1. While the paper covers a wide range of datasets, the study could benefit from exploring a wider range of uncontrolled domain shifts existing in more complex real-world datasets, like ImageNet or VisDA-2017.\n\n2. The claim that heuristic augmentation methods are more effective than the advanced generalization techniques is in fact very strong, challenging many areas of machine learning. To convince practitioners, it is important to conduct experiments on more complex real-world datasets.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The paper introduces an evaluation framework for understanding the robustness of machine learning models under more than one real-world distribution shift attributes (concurrent domain shifts) because in real-world scenarios, models face multiple unseen domain shifts combined with spurious correlations. \n\n### Contributions\n\n1. The proposed work performs extensive ablation studies to investigate how various data distribution attributes contribute to concurrent domain shifts. To be specific, the authors evaluate 26 different algorithms across 168 source-target pairs containing both synthetic and real-world datasets.\n\n2. The proposed study presents two interesting and important conclusions: first, concurrent domain shifts generally lead to poorer performance. However, models that improve generalization for one type of shift often perform well with other shifts as well. Second, heuristic data augmentations are more effective than many advanced generalization methods.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "1. Conducting more experiments and analyses on more complex and large-scale datasets, such as ImageNet and VisDA-2017, would help capture a wider range of uncontrollable distribution shifts.\n\n2. Conducting more experiments using more recent large language models, such as GPT and Llama, for zero-shot evaluations.\n\n3. Including a more detailed discussion on the limitations and potential failure modes of the evaluated algorithms, particularly regarding the heuristic data augmentation methods and their effectiveness in different scenarios.\n\n4. Including specific case studies or examples that show how the proposed evaluation framework can be applied to real-world problems with uncontrollable domain shifts.\n\n5. Providing visualizations to better illustrate the experimental results. For example, t-SNE plots could show how domain shifts affect the clustering of latent features.", "broader_impact_concerns": "N/A", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "g0j2ZnKNgx", "reviewer": "Reviewer_JanM", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "**Strengths**\n* The paper reads very well, the story is easy to follow and results are presented clearly such that the conclusions are easy to observe as a reader\n* Research approach is systematic and offers a spectrum of experimentation from very toy-like to real-world settings\n* Conclusions that relate to concurrent distribution shifts are interesting — notably that if a methodology helps for one setting it tends to help with others.\n\n**Weakness**\n1. Foundation models for real-world dataset can be misleading since the tasks are so niche (e.g., cancer detection, satellite image). Perhaps a few-shot or fine-tuning stage would be required for this set of results.\n2. Appendix section A.2 is slightly confusing. It seems the dataset sizes are pretty small (<100 samples in some cases?). Could you comment on how the dataset scale (assuming the same shift) might impact your results?\n3. Takeaway 1: “ConDS is, on average, more challenging than UniDS”, could’ve be explored in a bit more depth. This setting with concurrent shifts being more challenging is somewhat expected, but finding out how these shifts interact together would be very useful for the community — are they additive, compounding? Could you comment a bit on that here?\n4. The authors does not discuss possible higher level shift that might occur when inducing shifts across different attributes. Is it possible that setups with concurrent shifts across different attributes induces additional shifts of attribute pairs that could significantly influence results? That possibility should be discussed in the results. Could you comment on this here?\n5. In Takeaway 2: “We see that the presence of SC tends to dominate over other DSs in ConD” was only looked through the lens of absolute performance regressions. The correct measure here would be comparing relative regressions (UNIFORM -> LSS+UDS VS SC -> SC+LDD+UDS).\n6. Related works could use a bit more work, specifically around literature that investigates the types of distribution shifts, e.g., [1], [2], [3]\n\n[1]Ye, Nanyang, et al. \"Ood-bench: Quantifying and understanding two dimensions of out-of-distribution generalization.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022.\n\n[2] Yao, Huaxiu, et al. \"Wild-time: A benchmark of in-the-wild distribution shift over time.\" Advances in Neural Information Processing Systems 35 (2022): 10309-10324.\n\n[3] Recht, Benjamin, et al. \"Do imagenet classifiers generalize to imagenet?.\" International conference on machine learning. PMLR, 2019.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The paper presents an orthogonalized analysis on the impact of concurrent train/test data distribution shift. They focus their investigation on Spurious Correlation (SC), Low Data Drift (LDD) and Unseen Data Shift (UDS) and their impact on performance when multiple of these shifts appears between the training and testing set. They start from controllable (toy synthetic in some case) datasets where they have access to sample attributes and manually induce distribution shifts and study its impact on performance. They also include well known real-world distribution shift datasets (from WILDS) in their study. The authors then apply a wide array of robustness techniques and foundation models to these tasks. They observe that:\n* Concurrent distribution shift is more challenging and isolated shift, although most of the challenge comes from spurious correlations\n* If a methodology helps for one setting it tends to help for others as well\n* Heuristic augmentations and pre-training (both imagenet pre-training and zero-shot foundation model levels) are effective \n* Generalization algorithms provide limited performance gains", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "1. You report that the experiments were ran with multiple seeds and different attribute-shift pairings. Please report error bars in plots and tables.\n2. Running experiments with some sort of adaptation of foundation model to the real-world datasets (See point 1 of Weakness).\n3. Add clarification to Appendix section A.2. Specifically what is a split, because it is not defined in Section 3. (See point 2 of Weakness).\n4. Measure relative regressions of results use to make Takeaway 2 (See point 5 of Weakness)\n5. More thorough related works (See point 6 in Weakness)\n\n**Nit**\n* In Section 1, the sentence for finding (i) is difficult to parse.\n* In Table 2, it would make it much clearer for the attribute name to be in the tables along with the values they can take.\n* If possible, a very rough estimate of the total compute time would be great to have in there alongside the type of compute used.", "broader_impact_concerns": "I do not see concerns on ethical implications of this paper.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "1vEEXfU09r", "reviewer": "Reviewer_yz3M", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "1. The introduction of Concurrent Distribution Shifts (ConDS) is a novel contribution that reflects the complexities of real-world data more accurately than traditional single-shift frameworks. This approach provides a new perspective on evaluating machine learning models under more realistic conditions.\n2. The inclusion of both synthetic datasets (e.g., dSprites) and real-world datasets (e.g., CelebA, DeepFashion) ensures that the framework is tested across diverse contexts. This enhances the generalizability and applicability of the findings to different domains.\n3. The study’s findings are well-supported by detailed analysis, highlighting that heuristic augmentations and pre-training are particularly effective across shifts. This provides practical guidance for researchers and practitioners looking to enhance model robustness.\n\nWeaknesses\n\n1. While the paper covers a diverse set of datasets, the real-world datasets used have limited attributes for creating complex distribution shifts. This somewhat constrains the applicability of the findings to more varied real-world conditions. Future research could address this by incorporating additional real-world datasets or employing techniques to generate or simulate more attributes, thereby expanding the framework’s practical relevance.\n\n2. The reliance on pre-annotated attributes to create distribution shifts may limit the framework's applicability to datasets without such detailed annotations. This approach may not capture more subtle or complex shifts that emerge from latent features or learned representations.The authors could consider exploring methods that incorporate learned attributes, which could make the framework more adaptable and applicable to a broader range of datasets.\n\n3. The paper finds that foundation models, while effective in controlled settings, tend to underperform in real-world datasets. This is a significant limitation, as it highlights the gap between theoretical generalization and practical applicability in diverse domains. Additional strategies for tailoring foundation models to specific real-world contexts could enhance the robustness and practical utility of these models.\n\n4. While the paper evaluates a wide range of algorithms, there is limited discussion on adapting specific algorithms to better handle ConDS. Addressing this aspect could offer more tailored insights and potentially improve performance under complex shifts.\n\n5. The results might be sensitive to hyperparameter settings, especially in scenarios involving complex distribution shifts. An explicit analysis of hyperparameter sensitivity would provide greater transparency and make the results more robust.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper expands on the traditional concept of distribution shifts (DSs) by defining Concurrent Distribution Shifts (ConDS), where multiple types of shifts occur simultaneously within the dataset. This novel framework provides a more realistic representation of real-world data complexities compared to traditional Unique Distribution Shifts (UniDS), where only a single type of shift is analyzed.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "1. The inclusion of more real-world datasets with diverse attributes or using data augmentation techniques to create additional attributes would significantly enhance the framework’s relevance and applicability.\n2.  Consider exploring the use of learned attributes or unsupervised feature extraction to induce distribution shifts. This could be implemented using models like autoencoders or self-supervised learning techniques.\n3. Provide more in-depth analysis on why foundation models underperform in real-world scenarios compared to controlled datasets. This could include experiments with different training regimes or prompts that might mitigate these issues.\n4. Include a section that explores how sensitive the results are to hyperparameter changes, particularly under complex distribution shifts like ConDS. This could involve reporting performance variations across different parameter settings for key algorithms.\n5. Add clarifications on the benchmarking methodology, such as the choice of metrics used for evaluation and any considerations for dataset standardization. A table summarizing these choices and rationales could be helpful.", "broader_impact_concerns": "None", "additional_comments": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper performs an extensive study of robustness of various models across concurrent distribution shifts. Contrary to prior work that focused on evaluating robustness to one distribution shift at a time, this work explores the behavior of various models against multiple concurrent shifts. They conduct this analysis first in a controlled (synthetic) environment and then in more realistic datasets (commonly-used datasets from WILDS), and draw various conclusions. Notably, they find that indeed, concurrent shifts are for the most part more challenging than single shifts. They also find that robustness against one type of shift transfers to other types and that heuristic augmentations perform strongly.

The reviewers found that the paper "reads very well [...] and results are presented clearly such that the conclusions are easy to observe as a reader" (Reviewer JanM). 

They also found that the proposed framework "offers a more realistic method to evaluating model robustness" (Reviewer 6Dtq) and "provides a new perspective on evaluating machine learning models under more realistic conditions" (Reviewer yz3M). They also found that the paper "provides important insights" to better understand domain generalization issues and how to handle them, "contributing practical values to the community" (Reviewer 6Dtq). 

The reviewer also noted that the analysis is comprehensive ("a solid analysis of various types of domain shifts", Reviewer 6Dtq; "a detailed analysis of the limitations of vision-language foundation models", Reviewer 6Dtq; "tested across diverse contexts" (Reviewer yz3M)) and that "the research approach is systematic and offers a spectrum of experimentation from very toy-like to real-world settings" (Reviewer JanM). Reviewer yz3M noted that "the study’s findings are well-supported by detailed analysis".

The reviewers also identified some weaknesses, many of which were addressed through the rebuttal. Notably, the reviewer thought the paper could benefit from more complex real-world datasets (Reviewer 6Dtq, Reviewer yz3M), more recent large models (Reviewer 6Dtq), the inclusion of more experiments that adapt foundation models to the niche datasets considered before evaluation (Reviewer JanM). The reviewers were satisfied with the discussion during the rebuttal and the authors conducted additional experiments too e.g. to demonstrate the improved performance of foundation models on niche tasks after an adaptation phase. Other concerns that were addressed during the rebuttal include additional discussion of limitations, of the size of datasets, inclusion of additional details on hyperparameters and dataset setup, additional visualizations, and various clarifications.

All reviewers either recommend acceptance (or learn towards acceptance). The remaining weakness noted in the recommendation of Reviewer 6Dtq is the missing experiments on large-scale datasets. Overall, this paper meets the bar of acceptance for TMLR based on the above discussion.
Decision: Accept (poster)