Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 8: Strong Accept: Technically strong paper, with novel ideas, excellent impact on at least one area, or high-to-excellent impact on multiple areas, with excellent evaluation, resources, and reproducibility, and no unaddressed ethical considerations.
review_content: summary: This manuscript focuses on to provide an elegant approach to visually attributing the classification result of a frozen, pre-trained,
black-box classifier called as Explanandum on the input image(s). The author provides a solid method for the second deep network (called "Explainer") to provide a robust mask and attributions for test images. Together with solid experiments and solid baselines, the author achieves competitive results for attributions on the well-known datasets of PASCAL VOC and MS-COCO. 


strengths_and_weaknesses: Clarity: 
++ The paper reads very well and provides a very good description of related work and background, motivating the problem. Even outside of the contribution of this paper, I would recommend this paper to people getting started with deep learning/understanding black-box classifiers as it provides a thorough description of the part of the pipelines it deals with.

Novelty: 
++ The proposed approach formulation is concise, convincing, and novel. A seemingly reasonable approach has been conducted in this manuscript. Compared to the prior work on performing attributions for deep learning image classifiers, the current strategy involves supervised meta-learning to produce dense class-specific attribution masks, which balances the preservation and deletion of information from the input image(s) 
++ The idea of incorporating the Explainer to generate masks and retraining the Explanandum with the original as well as Explainer-masked data can ensure that classification accuracy can be high and free of influences from non-object regions. A pretty decent methodology has been applied to this attribution problem by the authors on this.

Experiments: 
++ There are a number of experiments performed across datasets that are extensive, fair, and provide solid foundations. The fact that the proposed methodology achieves competitive results makes me confident in the result as the implementation and experiments are sufficient and presented clearly. Additionally, the improvements are fairly consistent. Besides, in-depth analyses have been provided on the approach for different tasks and enough information has been provided via a thorough analysis of where the benefits of the approach have been obtained.

Reproducibility: 
++ Thanks to the author's efforts in giving enough clarity about the given methodology, this can be replicated with some efforts. I believe this could be a significant contribution this time in NeurIPS. 
questions: There is a small concern I faced in this manuscript about training the explainer, but little-to-less information has been provided on the black-box classifier (Explanandum), as we could see the explainer is trained to generate the masks, the retaining of the Explanandum with original + explainer-masked data information is missing in this section. Could I please request the authors to give some views on this as I tried to identify this root cause but found no such information on this? 
limitations: My assumption is that the author neglected on the part about tiny objects/having occlusions etc. I believe this methodology works great in general but general scenarios could also have tiny object and objects with occlusions, can the author please provides some views on this. Even though this approach sounds significant and I'm more inclining towards accepting this paper, as I found out this could potentially be a significant contribution to the computer vision community.
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: The authors propose to train a deep network - the explainer to estimate attributions for a pre-trained black-box classifier. The attributions are defined as image masks, in particular two masks - the class-relevant mask and the rest. Local and smoothness constraints assist the explainer in generating crisp, highlight localized attribution masks. The authors evaluate the approach on multiple datasets and compare with standard explanation methods. 

strengths_and_weaknesses: Strengths

The ability to automatically generate reasonably accurate segmentation masks (albeit in an opaque manner) is a key strength of the approach. 

The authors have experimented with multiple datasets and architectures to validate the method. 

The paper is well-written and structured. It is easy to follow.

Weakness

It is interesting to note that Grad-CAM results in comparable segmentation masks without the need to train an additional mask generator (Table 1). Grad-CAM extrapolates the low dimensional mask to the original image size that adds some noisy artifacts. Unlike the proposed explainer, it does not enforce any locality/smooth constraints. Despite these artifacts, Grad-CAM a very simple approach in contrast to the proposed explainer, can generate good segmentation masks. Thus, the proposed method’s advantage is unclear.

While the authors provide a quantitative comparison of existing approaches through the segmentation dataset, as the aim is to provide a human interpretable explanation, a user study to validate the same will strengthen the contribution.

Finally, the approach reads as a blackbox (mask generator) trying to explain another blackbox (classifier).


questions: Lines 29 and 30 - The authors portray the dependence of the explainability methods on trained model architectures and weights as undesirable. I however, would argue that this is desirable to enhance the faithfulness of the explanations tied to the trained model. The authors have to either elaborate more on their claim or provide ‌references supporting their claim. The subsequent sentence (line 31) is more appropriate. There is evidence suggesting that the explanations generated through certain methods appear to be independent of model parameters (Adebayo et al., and Sixt et al).

The third advantage (line 53) is confusing. If explanandum is not required, then how does one ensure the faithfulness of the explanations to the explanandum? Perhaps the authors refer to a model-agnostic explainer?

Line 65- Perhaps the citation is incorrect. Adebayo et al [1] do not handle issues arising from high sensitivity to noise and clipping effects of gradients. 

Line 114 - target label(s) for the input image - are these the predicted labels of the explanandum or the ground truth labels? If it is ground truth labels, then the explainer can never be faithful to the explanandum - what is the explainer then explaining? The authors should clarify if their method is post-hoc or explainable by design?

I reiterate the initial concern on faithfulness based on the observations in lines 475-482 (supplementary material). The explainer does not seem to be faithful to the explanandum!

Furthermore, I notice almost no highlights for regions other than the top predicted class (Figures 4, 5 supplementary material). How does the model explain non-zero probabilities assigned to the other classes?

limitations: not applicable.
review_title: null

final_score: rating: 7: Accept: Technically solid paper, with high impact on at least one sub-area, or moderate-to-high impact on more than one areas, with good-to-excellent evaluation, resources, reproducibility, and no unaddressed ethical considerations.
review_content: summary: The paper proposes a method to identify regions of the images that identify and contribute decisively at identifying image by the decisive  object. The task is approached by using two network entitled Explanandum (i.e. the model to be explained) and Explainer (the model that explains) and training process based on 4 losses. The method is evaluated in conjunction with Pascal VOC 2007 an MS-COCO 2014 datasets
strengths_and_weaknesses: Strengths:
 - the idea of using another network to infer the internal mechanism of a base one is very appealing

Weaknesses:
 - on the technical side, I view this paper contribution more closely to "incremental" than "outstanding". The idea to use two networks in a differentiating analysis is known.  None of the loss terms used in optimization are new; the approach is related to image saliency and object segmentation
- given the limited contribution on the idea side, the evaluation should have been stronger. PASCAL - VOC 2007 is the smallest version, MS COCO is larger, but only parts of it were used. On the visual comparison (fig 3- main paper), the proposed method is the sharpest, but EP, with another threshold on the heat map gives very similar results. 

Overall, the paper is not so strong on either side to be viewed as a clear accept.  

Other small issues:
 -  If accepted, the evaluation section should be re-written to enhance the strengths (positive part). Also, the explanation of experiments details (database presentation, .. l196-205)   needs to be separated  from the actual experiments 
 - figure 2 - is rather poor: Who is \mathcal{F} in the figure? \mathbf{m}? \mathbf{S}?
questions: 1. To me the details of the problem to be solved are not firmly established. The paper says  that it uses the  "multi-label classification tasks" version of the two databases. Yet, only single class examples are shown in the evaluation. Than it speaks about "attribution" and at last about segmentation. I am missing some phrases (in different parts of the text) that: 
 - say simply what the paper tries to solve
 - how can one evaluate the quality of this solution and why. The evaluation section carries the reader between different metrics and (unfortunately) excuses why some tests couldn't have been run in full. 
- how can one use the heat masks found


2. The method is not explained well:
- l.104 "given pre-trained classifier" 
- l 108 "Explainer sees an image and outputs a set of masks S". 
 Then how the Explainer relates to the pre-trained classifier!? It seems to me that the two are independent. The first time \mathcal{F} appears  is at line 133, again it is not explained well
limitations: The paper (approached problem and solution) does not raise any ethical or societal impact. Therefore, there is no (need for) discussion from this point of view.

The paper discusses technical limitations.
review_title: null

Meta-review: metareview: The paper proposes an attribution prediction approach to enhance the interpretability of DNN models. For this purpose, a second “explainer” model is used which can generality class-specific masks for the classification of relevant regions. 

The reviewers have overall commended the novelty of the approach, clear writing, and detailed experiments. However, there were concerns about the training required for explainability which makes the approach relatively more computationally demanding. Given the performance is better than GradCAM, the approach still offers an advantage and a suitable alternative. The rebuttal provided further clarity and corrections in light of the initial reviews, these changes must be incorporated in the final version. The AC will also support incorporating a user study since the final goal of these attributions is to provide visual explanations for human users. 

Based on the reviews and rebuttal, AC recommends accepting the paper and would like to congratulate the authors!

confidence: Certain
Decision: Accept