EXPLAINING IMAGE CLASSIFIERS BY COUNTERFACTUAL GENERATION
When an image classifier makes a prediction, which parts of the image are relevant and why? We can rephrase this question to ask: which parts of the image, if they were not seen by the classifier, would most change its decision? Producing an answer requires marginalizing over images that could have been seen but weren't. We can sample plausible image in-fills by conditioning a generative model on the rest of the image. We then optimize to find the image regions that most change the classifier's decision after in-fill. Our approach contrasts with ad-hoc in-filling approaches, such as blurring or injecting noise, which generate inputs far from the data distribution, and ignore informative relationships between different parts of the image. Our method produces more compact and relevant saliency maps, with fewer artifacts compared to previous methods.
Summary: This paper aims to find important regions to classify an image. The main algorithm, FIDO, is trained to find a saliency map based on SSR or SDR objective functions. The main novelty of this work is that it uses generative models to in-fill masked out regions by SSR or SDR. As such, compared to existing algorithms, FIDO can synthesize more realistic samples to evaluate.
I like the motivation of this paper since existing algorithms have clear limitations, i.e., using out-of-distribution samples. This issue can be addressed by using a generative network as described in this paper.
However, I think this approach yields another limitation: the performance of the algorithm is bound by the generative network. For example, let’s assume that a head region is important to classify birds. Also assume that the proposed algorithm somehow predicts a mask for the head region during training. If the generative network synthesizes a realistic bird from the mask, then the proposed algorithm will learn that the head region is a supporting region of SSR. In the other case, however, the rendered bird is often not realistic and classified incorrectly. Then, the algorithm will seek for other regions. As a result, the proposed method interprets a classifier network conditioned on the generative network parameters. Authors did not discuss these issues importantly in the paper.
Although the approach has its own limitation, I still believe that the overall direction of the paper is reasonable. It is because I agree that using a generative network to in-fill images to address the motivation of this paper is the best option we have at this current moment. In addition, authors report satisfactory amount of experimental results to support their claim.
Quality: The paper is well written and easy to follow.
Clarify: The explanation of the approach and experiments are clear. Since the method is simple, it also seems that it is easy to reproduce their results.
Originality: Authors apply off-the-shelf algorithms to improve the performance of a known problem. Therefore, I think there is no technical originality except that authors found a reasonable combination of existing algorithms and a problem.
Significance: The paper has a good motivation and deals with an important problem. Experimental results show improvements. Overall, the paper has some amount of impact in this field.
Pros and Cons are discussed above. As a summary,
Pros: 
+ Good motivation.
+ Experiments show qualitative and quantitative improvements.
Cons: 
- Lack of technical novelty and justification of the approach.