Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: The paper proposes a method to predict if a sample of in distribution or out of distiribution. The authors decomposes the features via SVD and remove the largest eigenvalue and its rank-1 matrix from the features. They show the theoretical effect of their method in the bound of their log probabilities. They surpassed the baseline methods in FPR and AUROC by 7-18%, and did an ablation study for the effect of higher ranks and other feature layers.
strengths_and_weaknesses: Strengths:
The method is simple and intuitive, the results also align with intuitions and achieve high improvement over the current baselines. The authors supported and motivated their ideas well. for example,  Figure 1 and 2 are good motivations and pictured well. The paper is written clearly and organized. 
The ablation study for removing rank-1 vs including only rank-1 and rank-k (swiping k) cases is very helpful to answer initial questions on the reader's mind. 

Weaknesses:
Although the complexity of power iterations is reasonable, they may converge very slowly if the eigenvalues have close values.
questions: -In the bound (equation 13), it looks like if s1 gets closer to the other eigenvalues, the bound seems to loosen more if the sum of other eigenvalues are smaller than the s1. Is it a case not encountered in the problems?

- What is the dataset you used for the Figure 1?

- Did you observe any problems with the convergence of SVD decomposition?

- How are your results without the fusion of 3rd and 4th block? How does the fusion help?

- The algorithm does better in terms of FPR compared with AUROC, this might suggest that the distribution gets squeezed and biased. How  do you explain this?
limitations: The authors did not foresee any negative impacts of their work.
review_title: null

final_score: rating: 3: Reject: For instance, a paper with technical flaws, weak evaluation, inadequate reproducibility and incompletely addressed ethical considerations.
review_content: summary: The authors present a new OOD detection method `RankFeat`, which removes the largest singular value and the associated singular
vectors from high-level features extracted from a classifier pre-trained on a labeled dataset (e.g. ImageNet-1k)
strengths_and_weaknesses: The presented method is interesting, but the evaluation is extremely limited and fails to position the performance of the proposed method in relation to all the OOD literature
- The paper mainly evaluates on the dataset first proposed on [1]
  - This dataset is very new, few works use it for evaluation
  - This dataset is only a far-OOD dataset, i.e. an easy dataset where OOD samples are far from ID samples
  - I won't be convinced until a more through evaluation is done that includes near-OOD datasets, specifically
    -  The standard one class vs the rest [2] is done in at least CIFAR10, and CIFAR100 (prefereably also ImageNet-30), as can be seen in e.g. [3]
    - The whole CIFAR10 vs CIFAR100 (and vice-versa)
  - It would be very interesting to show also evaluations that try to expose usefulness of pre-trained models for OOD  
    - The Species dataset, proposed in [4]
    - One class SVHN, porposed in [5]
- The paper compares only to a handful of other methods, many SotA methods in literature are ignored
  - Paper must compare to other works that tries to adapt pre-trained classifiers, e.g. PANDA [6]
  - Paper must compare to completely unsupervised methods, e.g. CSI [3]
  - Paper should explore the situation where ID dataset is polluted
- How can the method be described as post-hoc, when authors needed to fine-tune the model for 100 epochs on CIFAR before evaluation on CIFAR ?
- I find this strange, but why the authors doesn't compare to the method described in [1] (the original paper of the evaluation dataset) ?
- CIFAR results in the appendix aren't convincing in superiority compared to ReAct


[1] Mos: Towards scaling out-of-distribution detection for large semantic space. CVPR 2021

[2] Systematic construction of anomaly detection benchmarks from real data. In KDD 2013

[3] Csi: Novelty detection via contrastive learning on distributionally shifted instances. NeurIPS 2020

[4] Scaling Out-of-Distribution Detection for Real-World Settings. ICML 2022

[5] No Shifted Augmentations (NSA): compact distributions for robust self-supervised Anomaly Detection. arXiv 2022

[6] Panda: Adapting pretrained features for anomaly detection and segmentation. CVPR 2021
questions: All suggestions are in the weaknesses section
limitations: N/A
review_title: null

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: The paper claims that OOD and IND involve different largest singular value of feature map matrix. Based on its foundational claim, the authors propose a simple OOD detection score devised by subtracting the corresponding rank-1 matrix from (the feature maps) of a given (pretrained) network.
strengths_and_weaknesses: [Strengths]

Good performance. It reduces FPR95 quite significantly for large-scale OOD testing environments.

Novel observation. To my best knowledge, this is the first paper to study OOD samples using the singular value of a feature (map) matrix. Particularly, according to the authors' claims, IND and OOD show different behaviors in terms of the largest singular value of feature map matrix.

Application Diversity. The proposed method can be applied to both ResNet and ViT based architectures.

[Weaknesses]

1. The paper does not provide precise notations for the proposed method in both main paper and supplementary. Particularly, it is very important to which matrix SVD is applied, but the paper is not clear on this point. (Namely, is it applied to a mini-batch of feature maps, the feature maps of whole train set, or a the reshaped feature map of a single sample? The singular value will be completely different depending on whether the shape of X is [B, CHW] or [C, HW].) Moreover, some notations (which are critical for understanding and implementing the method) are also not clear.

2. Its foundational claim (the largest singular value is larger in OOD than in IND) is not extensively validated. This is validated in only one experiment given in Figure 1(a). I am wondering if this claim would still hold even if different datasets are used for IND and OOD.

3. (minor) The paper does not give a convincing explanation of its foundational claim; namely, why OOD involves a larger singular value s_1 than IND? I saw the supplementary part for this, but the explanation is not so convincing.

4. (minor) Some of the given theory is not rigorous. Particularly, the convergence criterion to a random matrix given in the second part of the theory is not clear.


questions: 1. Could you specify the datasets used for ID and OOD for Figure 1 in its caption if possible? 
2. (Critical) What is B in the line 71? Is it batch size or the number of all samples? Does it include OOD samples? 
3. (Critical) Shouldn't the capital X in Eq. (4) be small x? Can RankFeat be computed for a single test sample x? What is y'? I know it is logit, but is it a set of logits of multiple samples or the logit of a single sample? How is it summed? What is the index of the sum in Eq. (4)?
4. (Critical) Overall, I don't precisely understand on which matrix exactly SVD is applied. Is it applied to the feature maps of training (or validation) set? Or is it applied to the feature map of a single sample? Is SVD computed only once and are u_1 v_1 s_1 fixed during whole inference? Or do you have to re-compute u_1 v_1 s_1 every time you make inference on a different test sample? This is very critical for accepting the paper. In fact, X with shape [B, C, HW] is not a matrix (2d tensor) but a 3d tensor. Do the authors use torch.svd on the matrix shaped by [B, C, HW]? 
5. Doex X in line 107 have to be a batch of feature maps? Or the theory would still hold even if X is the feature map of a single sample?



limitations: The limitations are given in the above 'weaknesses' and 'questions'. Please address all my questions and the second and fourth points of the weaknesses. In particular, please focus on clarifying your method, exactly which matrix SVD is applied to, and when SVD is applied.

I am quite positive with the paper except for the notation issue and the second point of the 'weaknesses'. I personally think scalar quantities should not be bold-faced; it is quite confusing.
review_title: null

final_score: rating: 5: Borderline accept: Technically solid paper where reasons to accept outweigh reasons to reject, e.g., limited evaluation. Please use sparingly.
review_content: summary: This paper aims to address OOD detection issue by proposing a novel method called RankFeat.

By observing the OOD feature matrix tends to have a significantly larger dominant singular value, the author believes that by removing the rank-1 feature, the over-confidence of OOD samples is mitigated, and consequently the ID and OOD data can be better distinguished.

Based on the above idea, the propose RankFeat, which aims to removing the rank-1 matrix from the high-level feature.

Theory has shown that it seems RankFeat achieves smaller energy score than ReAct.

Additionally, experiments have shown this method achieve a large improvement.

Lastly, ablation studies are conducted. 
strengths_and_weaknesses: Strengths:
1. This method is easy and the idea is clear.
2. The writing is good and organisation is also good.
3. Experiments are rich. 

The basic idea of this paper does give researchers a novel perspective to understand OOD detection. I also like this idea.

Weakness:
1. I still can not understand figure 1(b). Could you provide more detailed introduction?
2. I am interesting in more datasets to conduct experiments in figure 1(a). 
3. According to Eq. 9, your method is based on an assumption that the energy-based score is effective. Do you have checked other score function to implement your idea? or do you have argued that energy-based score is the optimal for your method?
4. The theory is not right The basic reason is that you cannot guarantee the bound in Eq 17 is the tightest bound, so you cannot use it to compare with your method's bound Eq 14. For example, a<1, b<2 cannot conclude that a<b. Here you have the same mistake.
5. The theory is weak. Eq 14 has shown that  RankFeat achieves small energy score . However, you omit a basic issue that why  a small energy score is good for OOD detection. You should use theory to explain it; otherwise, I think your paper has some unmentioned assumptions.
questions: The basic idea of this paper does give researchers a novel perspective to understand OOD detection. I also like this idea. But I have many issues raised.

Issues:
(Major)1. In the real world applications, the OOD datasets can be changeable and diverse. Could you guarantee the observation in Figure 1(a) still holds? I would very, very much doubt this observation. I think you should conduct much more experiments to support the basic view. In fact, if you can give theory (allowed assumptions) or abundant experiments to support the basic view, I think this paper can be accepted by NeurIPS; otherwise, I may not accept it.

(Major) 2. Experiments should be conducted to show why energy-based score is optimal for your method in Eq. 9.

(Minor) 3. In line 222, I find you remove features from 1 to n. How about only removing features n?

(Major) 4. The theory is over-claimed. Please revise it according to weakness 4 and 5.

I think this paper can be accepted if the authors can address all my issues.




limitations: In the real world applications, the OOD datasets can be changeable and diverse. Could you guarantee the observation in Figure 1(a) still holds? I would very, very much doubt this observation. I think you should conduct much more experiments to support the basic view. In fact, if you can give theory (allowed assumptions) or abundant experiments to support the basic view, I think this paper can be accepted by NeurIPS; otherwise, I may not accept it.
review_title: null

Meta-review: metareview: Thanks for your submission to NeurIPS.

This paper generated quite a bit of discussion, with several reviewers having lengthy discussions with the authors on various points in the paper.  At the end of the day, it seems that three of the four reviewers are mostly happy with the paper (with scores of 6, 6, 5, though the 5 reviewer indicated that they would raise their score to 6 but never did, so I'm assuming this is 6, 6, 6).  One of the reviewers was more negative, giving a 3.

The biggest issue of the negative reviewer revolves around the experimental setup, and in particular the comparison of post-hoc and non-post-hoc methods (or lack thereof) in the experimental results.  Though I'm not sure the reviewer was ever fully satisfied with the resulting experiments, I do see the differences between the experimental methodologies and am satisfied that the experiments presented in the paper are sufficient and reasonable.  Given that the other reviewers are happy with the paper (and I've also read the paper and am OK with it), I will recommend accepting the paper.

When preparing a final version of the manuscript, please do keep in mind the many comments of the reviewers, and try to address these as much as possible.
confidence: Less certain
Decision: Accept