Venue: NeurIPS 2021 Conference

Reviews:

final_score: rating: 6: Marginally above the acceptance threshold
review_content: summary: The authors propose a Bias-Contrastive (BiasCon) loss, Soften Bias-Contrastive (SoftCon) loss and Bias-Balanced (BiasBal) regression to mitigate model biases.
main_review: The motivation is clear and the problem being solved is important.  The contribution seems iterative but results are strong.  I have some concerns/questions on some of the details and choices made.  Hopefully the authors can clear them up.

BiasCon pulls samples with same class but different biases closer and other pairs apart.  Why would it be preferable to pull samples with the same class apart regardless of bias?  It seems more intuitive to pull apart samples with same bias and pull together samples with same class which would also utilize same class samples without shared bias as well as different class samples with shared bias.  It would also remove the issue of limited positive pairs in a batch.  Can the authors comment on this?

Cross entropy (CE) should be pulling together samples with the same class which when combined with BiasCon in Equation 3 should balance the issue of the model learning to separate the representations of same class samples, but it seems that biasCon here would reduce the effect of the CE loss.

In the case when the dataset has limited examples of same class pairs without shared biases, which may be the case in many biased datasets, couldn't oversampling these datapoints lead to other learned biases specific to the limited data.  Essentially the model may become overtrained on these limited datapoints.

It is confusing to refer to P_u as the unbiased test data distribution as it is defined using only the training data.

Equation 8 is summing logits with probabilities.  Logits can be large, small and possibly negative depending on the model.  How is this taken into account here so that the combination with p(y|b) has an influential contribution?

Could the bias-capturing model also be used to weakly label positive bias pairs in biasCon?  More info on the bias-capturing model would be helpful.

Without these labels how can we trust that the representations of the bias-capturing model are clustered according to biases?  Many close representations could be simply datapoints of a similar class.  How is it ensured that the bias-capturing model representation is highly sensitive to bias?  In many applications it is not as simple as reducing the size of the receptive field such as the case with gender or racial biases in data.
limitations_and_societal_impact: yes
review_title: null

final_score: rating: 6: Marginally above the acceptance threshold
review_content: summary: The paper propose two method, BiasCon and BiasBal, to mitigate the dataset bias. In particular, BiasCon is a contrastive-like objective that pulls a pair that shares the same target class but different bias feature. BiasBal objective is derived via assuming the data generating process. The authors also propose SoftCon, an extension of BiasCon, to tackle the unknown bias label. The proposed methods are evaluated on BiasedMNIST, CelebA and UTKFace dataset.

# POST-REBUTTAL
The additional results look good. After reading these and my co-reviewers comments, I still advocate for weak accept (score 6).
main_review: Overall, the paper address an important problem in machine learning. The paper is well-written and the contributions are stated clearly. I have a few questions and concerns.

Positive Points:

1. The proposed BiasBal is interesting. It seems that it is a simple yet effective objective for tacking the dataset bias.

2. The proposed approach can also be partially extended to the case when the bias label is missing. It could be very influential since in real world application such as medical system, the bias label (e.g., gender) is not accessible.


Negative Points:

1. The paper does not discuss or compare to existing works about fairness. The bias variable is essentially the sensitive attributes in fairness literatures, e.g., the gender attribute in CelebA and UTKFace experiments. There are existing works [1,2,3] that leverage the sensitive attribute to learn fair representations or build fair classifier. 

2. Besides machine learning fairness literatures, there is another line of works that solves similar problem under the domain generalization or casual inference framework. I encourage authors to include some standard baselines such as Invariant Risk Minimization [4] or Domain-invariant Networks [5]? In particular, these works treat the domain-specific features as biased features. Therefore, the variable B could be domain index.

3. The main metric used in the paper is the unbiased accuracy. It would be great to measure and discuss the performance w.r.t. demographic parity or equalized odds on CelebA and UTKFace dataset. To ensure machine learning fairness, unbiased accuracy only provides little informations.

Other comments:

1. I suggest moving the graphical model in appendix to section 3.3. This would help readers understand equation (5) and (6) more.

2. It would be great to perform some analyses to the BiasCon objective. For instance, maybe one can link this to the mutual information between the feature and feature bias B?

3. The notation is a bit confusing. It seems that $p$ has been used to denote several distributions.

Overall, I think this paper address an important problem that will be of interest to a wide audience in NeurIPS. The method is stated clear and the experiments are sound. Nevertheless, there is lack of discussions and comparison to previous works. The paper would be much more polished if the authors can provide more empirical comparison to other baselines and metrics. 


[1] Zemel et al., Learning Fair Representations, 2013

[2] Madras et al., Learning Adversarially Fair and Transferable Representations, 2018

[3] Creager et al., Flexibly fair representation learning by disentanglement, 2019

[4] Arjovsky et al., Invariant Risk Minimization, 2020

[5] Ganin et al, Domain-Adversarial Training of Neural Networks, 2015

[6] Li et al., Information-Preserving Contrastive Learning for Self-Supervised Representations, 2021

limitations_and_societal_impact: The authors adequately addressed the limitations and potential negative societal impact.
review_title: null

final_score: rating: 7: Good paper, accept
review_content: summary: This paper proposed several debiasing losses for unbiased image classification. First, this paper proposed two losses for known bias labels. motivated by contrastive learning, the Bias-Contrastive (BiasCon) loss for better representation learning. The Bias-Balanced (BiasBal) regression is proposed for highly imbalanced target-bias correlation. Second, the Soften Bias-Contrastive (SoftCon) is proposed for unknown bias labels. Experiments on BiasedMNIST, CelebA and UTKFace show that the proposed debiasing method outperforms state-of-the-art methods by a large margin whenever the bias label is available.
main_review: Strengths:

+ This paper reveals the potential and effectiveness of contrastive learning in debiased image classification. The contrastive-learning-based loss is simple but very effective.

+ Experimental results on several benchmark datasets show that the proposed methods outperform state-of-the-art debiasing methods by a large margin with or without bias labels. 

+ Ablation studies are sufficient to show the effectiveness of each component.

Weaknesses & Questions:

- The connection between BiasCon and BiasBal losses are not tight enough. It seems that these two losses are like a bag of tricks rather than a whole. Also, BiasBal is not used without bias labels. Even without BiasBal, I still think this paper is good. However, BiasBal seems a little incompatible in this paper. Perhaps a better introduction of BiasBal would help with this.

- Another question on BiasBal. While BiasBal achieves competitive results in Tables 2 and 3, the performance in Table 1 is much lower than BiasCon when the bias and label are highly correlated. Recall that BiasBal is proposed to tackle the issue of a highly imbalanced correlation between label and bias. Is there any explanation for this?

- The performances of Vanilla models with bias labels were implemented (Table 1-3) while those without bias labels were borrowed from ReBias (Table 4, 5). I wonder (1) why the vanilla models were not implemented in Tables 4 and 5, (2) whether the increase comes from the implementation. Results of vanilla models obtained by the same reimplementation would answer these questions.

- It is not clear how Eq. (5) and (6) are theoretically correct. In Eq. (5) and (6), why can we replace $P_{u}$ with $P_{train}$?

- After reading the paper, I am supervised by the success of contrastive learning in debiased image classification, but still not clear why contrastive learning can outperform others by a large margin (e.g., 93.3 vs. 52.3 on BiasedMNIST with corr=0.999 in Table 1). Is there a more intuitive or convincing explanation for this increase?

- As shown in Tables A4 and A6 in the appendix, the hyperparameters $\alpha$ are various for different correlations between label and bias. Is it a fair experiment setting? Or, is the hyper-parameter determined based on the test set? It is okay if this is the commonly used setting in other related works. In my opinion, the hyperparameters should be the same for different correlations as the correlations are supposed to be unknown in real-world applications.


==================== After rebuttal ======================

According to the authors' feedback, most of my major concerns have been addressed, and I am happy to vote for acceptance.

The strengths and contributions of this paper are:

(1) Two losses for known bias labels and one variant for unknown bias labels for unbiased image classification. BiasCon is proposed for better representation learning motivated by contrastive learning, and BiasBal is proposed for highly imbalanced target-bias correlation. A variant of BiasCon, SoftCon, is proposed for unknown bias labels. These losses are simple but effective for unbiased image classification.

(2) The paper is well written and easy to follow. The contributions are clearly stated.

(3) Experiments and ablation studies are well designed and demonstrate the effectiveness of the proposed losses.

My major concerns were:

(1) The connection between BiasCon and BiasBal losses is not tight enough. According to the authors' feedback, I acknowledge the contribution of each loss to unbiased image classification. However, it would be greater if these two losses could be unified into one framework rather than two independent techniques. Due to this major concern, I would not give a higher score.

(2) The reason why contrastive learning works. Reviewer bmYk and YM7p raise a similar concern via a more detailed question: how and why the choice of BiasCon is determined, especially positive and negative pairs. The authors' feedback provides more experimental results to empirically compare BiasCon and other variants, which I think answers the "how" question. For the "why" question, I have left a post-rebuttal note to request the authors a more comprehensive and conclusive explanation.
limitations_and_societal_impact: The authors have adequately addressed the limitations and potential negative societal impact of their work.
review_title: null

final_score: rating: 8: Top 50% of accepted NeurIPS papers, clear accept
review_content: summary: The authors propose three debiasing methods. First, Bias-Contrastive Loss intends to bring the feature representations for samples with the same target class but different bias labels together. Second, the authors derive a Bias-Balanced Loss, which optimizes for unbiased distribution i.e., a distribution where target class and bias labels have uniform correlations. Third, they propose Soften Bias-Contrastive loss, which does not require bias labels, but instead uses a bias-capturing model to modulate the contrastive loss. The experiments are conducted both with and without bias labels and show improvements on all of the benchmarks.
main_review: Overall, the proposed techniques are novel and the experiments are well set up to demonstrate their efficacies. Building bias-resistant techniques based on the recently rising self-supervised methods is a valuable contribution to the field. Both BiasCon and BiasBal are well motivated and show improvements over other techniques that rely on bias labels. The SoftCon loss also improves upon other techniques that do not require bias labels. For this, SoftCon uses feature similarity/distance from bias-capturing models, which is different from how the previous methods e.g., LfF used them. Overall, the results are impressive, the techniques will be useful and the paper is very well written.

I will now move on to clarification questions, addressing which may help improve the paper:

[Q1] The very first question I had with the contrastive loss was: did the multi view batches already encode invariances? E.g., using grayscale for BiasedMNIST would be a trivial solution. While the authors mention the exact augmentation strategies in the supplementary section, it might be worthwhile to have at least a sentence stating whether or not the creation of the multi-view batch itself would help learn the signal in the main text itself.

[Q2] For the Biased Contrastive Loss, it wasn’t clear why the samples with the same class, same bias were used as negatives. Intuitively, all instances from the same class should have similar representations, so should be used as positive pairs. What happens if the same class, same bias samples are used as positives instead? Which one is better and why?

[Q3] While the oversampling ablation is mentioned in the supplementary, it might be worthwhile to have resampling/loss reweighting techniques as comparison methods for all the datasets. 
While simple, loss reweighting can mitigate biases under sufficient regularization [1], and thus might have practical utility. Also, the intuition behind the simple, classical techniques of resampling/reweighting based on target and bias labels is the same as the goal of Theorem 1 i.e., to optimize for uniform correlation between target and bias labels. As such, both theoretical and empirical comparisons with them would be a huge plus. It would be particularly interesting to have theoretical analyses supported by empirical results as to why/if BiasBal loss is better than the classical techniques. What is lacking in these classical techniques, that are otherwise very intuitive?
At the very least, [1] should be cited here.

[Q4] The experiments section would be more insightful if the authors ran SoftBiasCon on datasets where it is not obvious how one can develop a bias-capturing model. The two datasets tested for SoftBiasCon had similar bias-encoding models (geared towards capturing background color/texture), but it is not obvious how an architecture could be developed for capturing “gender” bias in CelebA. Of course, perhaps the bias amplified model from LfF could be used for this case. In any case, it would be nice to have SoftBiasCon loss be run on all datasets, since bias mitigation without bias labels would be highly desirable in practice.

[Q5] While the bias labels were not used during training for the SoftCon loss, were they used for model selection/hyperparameter tuning? It would be good to explicitly mention this.

[1] Sagawa, Shiori, et al. "An investigation of why overparameterization exacerbates spurious correlations." International Conference on Machine Learning. PMLR, 2020.
limitations_and_societal_impact: While the authors mention not testing on more realistic benchmarks e.g., VQA, it might be worthwhile to also add that in such cases, biases may stem from multiple sources and it may not be trivial to label all of them or design bias-capturing models for all of them (which are required by the proposed methods).
review_title: null

Meta-review: The paper proposes new loss functions for training on datasets with feature bias. The reviewers find the proposals to be novel and the experiment results to be strong, and recommend acceptance. There were some concerns raised about the rationale behind some of the design choices, and missing comparisons to some baselines. The authors have satisfactorily addressed these concerns over multiple back-and-forth discussions with the reviewers, and have provided additional supporting experimental results. We trust that the authors will include the additional results in the final version of the paper.
Decision: Accept (Poster)