Paper: Spawrious: A Benchmark for Fine Control of Spurious Correlation Biases (ICLR-2024)

Reviewer comment (Experiments):
Although the paper evaluates many group robustness methods in Table 3, none of them is designed to handle both correlation shift and domain shift. Why not evaluate the W2D method (Huang et al. 2022) that is designed to handle two shifts, which is the main focus of Spawrious?

Author response:
Thank you for highlighting this method to us; we have since collected results for W2D and added the results to Table 3 in the revised submission. Averaged across all Spawrious challenges, W2D ranks 6th out of 12 methods.

Reviewer comment (Experiments):
Wenzel et al. [1] did a comprehensive evaluation of OOD generalization. One of the conclusions is that architectures (such as Deit, Swin, and ViT) play a key role in improving OOD robustness. Although the paper benchmarks many group robustness methods and compares two architectures (Appendix D), I think it is necessary to see more results in terms of different neural architectures on the new benchmark based on conclusions in [1].

Author response:
Thank you for this suggestion! We have since included results in the appendix for the effects of using the Vit-B/16 architecture instead of the ResNet-50. The results we included in the initial submission remain the strongest.

Reviewer comment (Experiments):
To show that Spawrious is really challenging, I think we should evaluate the performance of foundation models (e.g., CLIP [2] with zero-shot transfer) pretrained on web-scale datasets.

Author response:
Thank you for suggesting an interesting direction for investigation! We shall return to you with the results of fine-tuning a head applied to a CLIP feature extractor on the Spawrious challenges. 

We believe there is some slight confusion – the problem setup of domain generalization assumes some distribution shift between training and test domains. Consequently,  training methods addressing this problem have only access to training domains that differ from the test domains. 
For a fair evaluation of such methods, the models must be trained on training data that strictly differs from the test data. In the case of CLIP, the training set is unknown and vast, consisting of internet-crawled data. Hence, the CLIP model may have already been trained on {class, background} combinations similar to the ones in the test data. 
However, this result may interest the OOD community, and we will attempt to gather results for the next revision.

Reviewer comment (Experiments):
**Failures of Dataset Design**: My first issue is about how to verify whether the output of the text-to-image model matches and satisfies **all** conditions or features specified in the prompt. The authors discuss this issue in Appendix F. The authors attempt a manual and an automatic filtering process. However, both of these also might be susceptible to failure in different ways.

Author response:
This is a valid point, and we appreciate your insight. Our automatic filtering process may fail due to errors from the captioning model, and the human filtering part might fail when we do not reach out to a variety of human volunteers to assess the alignment of the image-prompt alignment. We have since sent out 480 random samples of our dataset to human volunteers who have been asked to decide on the image prompt alignment and have found that 97.2 % of the dataset is clean. Additional details are provided in Appendix H of the updated submission.

Reviewer comment (Theory):
**Insights**: Table 1 is a very compelling result for mixup, and it points at trying to better understand its properties theoretically w.r.t. to spurious signals. Given the already wide scope of this paper, it is unable to delve into explaining the effectiveness of various methods.

Author response:
We could not find any works that theoretically analyze the OOD performance gains from JTT, nor specifically in the subproblem of robustness to spurious correlations. Zhang et al. (2020) [1] find that Mixup loss acts similarly to a second derivate adversarial loss, explaining adversarial robustness improvements gained from it. This seems unlikely to explain its performance on Spawrious, which is a spurious correlation benchmark. Perhaps the background information is mostly degraded in the mixing of the images while the dog features preserve better, in which case it is hard for the classifier to identify the background as easily as the dog features, making the dog features more useful for prediction. This could be tested by switching the benchmark to background prediction with spurious dogs, and measuring the ability of Mixup trained classifiers to rely on the backgrounds rather than the dogs. Given the time constraints, this experiment remains future work.

It is hard to say why JTT performs well on O2O but not well on M2M. Our speculation is that O2O can be partially solved by reweighting the training samples so that the backgrounds are less predictive, which is possible since the corr(background, label) <1, and there exist samples where the background is not spuriously correlated with the label, for all classes. For example, the beach background samples in O2O easy. Then, reweighting the samples so that the beach background samples have a larger proportion of the loss will reduce the utility of the background as a predictive feature and increase the utility of the dog features. How to find the right reweighting? JTT does this by letting ERM models overfit, making mistakes on the beach background, and then upweighting these samples accordingly. 

This strategy will not work on M2M however because the spurious correlation between groups is corr(B,C) = 1, thus all samples have spurious features and JTT cannot easily identify the samples that would rebalance the spurious correlation.

Reviewer comment (Evaluation):
In Table 3, how is this average computed? Also, does it make sense to report an average if it includes a combination of different settings/environments?

Author response:
This average is computed as the average of the row, giving the average Spawrious benchmark performance for a given optimization algorithm.

Reviewer comment (Experiments):
Section E of the appendix is very interesting. Did you also happen to compute the saliency maps for the mixup models on these same inputs? Would be interesting to compare both of them.

Author response:
Thank for raising an interesting direction of investigation! We are currently running a training loop for ERM and Mixup models, and will present saliency maps of their misclassifications, in order to investigate reasons for Mixup’s superior test performance.

Reviewer comment (Experiments):
Comparison with/discussion on some other relevant spurious correlation benchmarks that also use a synthetic/combinatorial construction pipeline like the PUG dataset is missing.

Author response:
We appreciate you highlighting the PUG dataset to us and have added a reference to PUG in the revised submission.
Firstly, the PUG dataset was released long after (>6 months) our benchmark has been publicly released and already adopted in several DG works. Unfortunately, the PUG paper missed mentioning our benchmark. 
Secondly, to answer your actual question, PUG does not introduce spurious correlations between class and background in their benchmark, and neither do they attempt to balance the datasets by class and group. In this regard, we feel that PUG addresses a different problem and Spawrious directly contributes to the OOD community.
Please let us know if there are other synthetic construction pipelines you have in mind and we will make sure to cite these.

Reviewer comment (Evaluation):
Some understanding of the hardness of the proposed benchmark would be relevant. Presumably there is some optimal re-weighting function for this dataset. How does the JTT assigned weights compare with this?

Author response:
Thanks for pointing this out! The hardness of the challenges differ due to the variation of the background features for a given location. For example, while the Jungle and Dirt location seems to have little variation in the features observed, with forestry and dirt paths, the Mountain background varies substantially. Further, there doesn’t seem to be much overlap between Mountain and Snow background features with other locations, while Jungle and Dirt often overlap with each other in grassy dirt scenes. We will clarify this further in the revision. 

The optimal (reweighting) importance weights can be inferred from Table 2 in our initial submission. We will add the JTT assigned weights to next revision.

Reviewer comment (Experiments):
Performance of some other recent baselines like RWY (uses group info), BR-DRO/LfF (does not use group info) is missing.

Author response:
Spawrious is designed to be class-balanced in O2O and both class- and location-balanced in M2M. Hence, re-weighting algorithms like RWY are not applicable. In other words, RWY rebalances the dataset for each class by its size in each group, but Spawrious classes are already balanced in each group. We will clarify this in the next revision, thanks for asking this.

We acknowledge that there are missing baselines from our benchmark, while curating those we believe are representative of the literature at large, such as IRM for group info optimization and JTT for group agnostic optimization. We intended to capture a wide variety of baselines to demonstrate the utility of our dataset. We look forward to extending our benchmark results to other methods (such as BrDRO) within a reasonable time frame. 

Unfortunately, we could not find any repository for the LfF code. BrDRO is exciting to look into, however, the documentation provided is sparse, making it challenging to evaluate it quickly within the rebuttal time. We already have evaluated JTT, another method that does not use group info.

Reviewer comment (Novelty):
The paper falls slightly short of building some understanding of the distinguishing characteristics of the proposed dataset—it is unclear how M2M is different from O2O but with superclass/superattribute labels. This limits insights into the benchmark’s unique aspects.

Author response:
We agree that the community lacks a SC benchmark, and thank you for your recognition. M2M is qualitatively different from the O2O case because the spurious feature is no longer fully predictive of the class. In the O2O case, setting the spurious correlation to 1 would render the background features equally as predictive of the class as the dog features. In the M2M case, setting the spurious correlation to 1 renders the background predictive of 2 classes but insufficient to distinguish between the two classes. In our dataset, we set the O2O spurious correlation to less than 1, so the dog features are marginally more predictive than the background features. We set the M2M spurious correlation to 1 because the dog features are already much more predictive than the background features.

Reviewer comment (Evaluation):
Uncertainty remains as to why the correlation is completely flipped in the M2M case only, and not in the O2O case.

Author response:
We agree that the community lacks a SC benchmark, and thank you for your recognition. M2M is qualitatively different from the O2O case because the spurious feature is no longer fully predictive of the class. In the O2O case, setting the spurious correlation to 1 would render the background features equally as predictive of the class as the dog features. In the M2M case, setting the spurious correlation to 1 renders the background predictive of 2 classes but insufficient to distinguish between the two classes. In our dataset, we set the O2O spurious correlation to less than 1, so the dog features are marginally more predictive than the background features. We set the M2M spurious correlation to 1 because the dog features are already much more predictive than the background features.