Sun Dec 8th through Sat the 14th, 2019 at Vancouver Convention Center
This paper examines the use of concept-based explanations rather than feature-base explanations. The algorithm in the paper, ACE, segments images, clusters similar segmentations, and then provides a list of explanations (salient concepts) by using TCAV. The paper had very good motivation. I think that the authors could also motivate the use of concpet-level explanations to mimic commonsense. For example, in the police van example, the individual features are not as important as the police logo and the general shape of the vehicle. Even if we are new countries and places with different polic vans, we can still abstract out the higher level concepts (like shape and logo) that distinguish the vehicle as a police type. The concept-based explanation desirata is a nice, concise section outlining the standards and evaluations for concept-based explanations. A small point, but the authors may want to distiguish the difference between a saliency property and a saliency map. When first reading the paper, I was confused whether the properties in the desiderata were types of methods or properties of the output. The methods secion provides a nice overview and definition of explanatory algorithm. I found the description and algorithm of ACE to be clear and straightforward. The authors state that the second step of ACE (the clustering of similar segmentations) can be replaced with a human subject, and I was wondering if this was tested or an effective augmentaion. That could be a nice sanity check on top of some of these concepts. In addition, the authors motivate the use of TCAV for a concept score. Although, they say "any other concept-importance saliency score could be used," I was wondering (1) why they choose to use TCAV rather than another salience score (e.g. Network Dissection) and what other scores could be used (or not) and why. I was also wondering about the last point about the reliability of CNNs as a similarity metric. I was left wondering if this method could point out adversarial examples. There were a few details in the experiment and results section that could be explained further. For example, why was using 50 images sufficient for extracting the related concepts? The authors state that this is because "the related concepts of a class tend to be present frequently in the class images," but I was wondering if this is dependent on the dataset. Similarly, I was wondering why the authos applied ACE to 100 ImageNet classes. Although I was delighted to see the results in the Appendix. I think the user study could use a bit more quantification, though. While I was convinced with the results, I was wondering how similar descriptions were between participants. Although the authors stated that "77% of descriptions were from the two most frequent words on average," I wasn't exactly sure what they meant across the experiment. The example was motivating, but I think a similarity score may strenghten the argument. In summary, I thought this was a nice paper that extended TCAV and promoted the use of concept-based explanations. Although I found the method and results to be sound, I was left wondering about future work and applications of such a method. I think the idea of using this to harden existing DNNs is a good one, but I was left wondering how that would be done.
The paper is clear, well written and addresses an important problem in ML research, specially for black-box models such DNNs. The authors propose a set of desiderata for concept-based explanation models and novel method to produce global explanations for vision DNNs. The proposed method accomplish all the criteria by replicating the manual process of finding relevant concepts byt replace humans with automated methods. The techniques used to implement the method are not novel but they were combined in a novel and relevant way. The works was thoroughly evaluated with appropriate methodologies, including an intrusion task to measure coherency, SSC and SDC to measure saliency and inspection of the learned model.
Originality: The paper is clearly linked to dictionary learning, unsupervised feature extraction, and also with the original LIME paper (where the interpretable features aka concepts are designed by hand). Regardless, the paper seems completely original. Quality: I really like the idea of providing explanations using higher level concepts, and also the fact that the authors did carry out experiments with human subjects. The proposed method is very, very simple (it is basically segmentation + clustering w.r.t. the inception distance + TCAV afterwards), but this is not necessarily an issue. I also really like Figure 6, which should be given more prominence, I think. Still, I do not really like the desiderata, which feel ill-defined, inconsistent, and overall vacuous: - Meaningfulness to me sounds like it is referring to concepts with a meaning---where both "concept" and "meaning" are not well defined. - Coherency, or actually "perceptual similarity": it seems to be much lower level than meaningfulness. For instance, cars definitely represent a meaningful concept, but they can be perceptually very different to each other. The same goes for most concrete concepts (person, chair, phone, ...). After reading meaningfulness and coherency, I don't know if the authors would consider "zebra pattern" as a high-level concept or just a texture pattern. These two desiderata don't seem to be helping my understanding. I feel like the desiderata don't add much to the paper (if not confusion), and should probably be compressed to two / three inline sentences. As things stand now, I feel like the desiderata detract from the presentation. Also, the proposed method is very image-centric; this is probably a liftable limitation. The second thing that I don't like is that, at a higher level, the paper carries a very bad message: that debugging models using automatically extracted concepts would remove the need for human intervention (line 47). The problem is that if the extracted concepts are not good (and segmenters *can* extract garbage, despite the experiments with human subjects presented here), then it is impossible for anybody to debug the learned model. I would prefer if the message was about helping or augmenting human experts, not about replacing them. It seems both harmful and irrealistic. Clarity: The paper is reasonably well structured, but the presentation is not always clear. For instance: - lines 27-28 are unclear - line 47: "ace **removes** the need to have humans look ...": way too strong; please reword. - line 61: "meaningfulness should also [...]", I cannot parse this sentence. - lines 108-131 are extremely dense and hard to parse; please rewrite. - line 122: "replaces of" - line 150: "examined at" Most importantly, the description of the human pilot experiments are *very* hard to follow, and should be heavily revised. Significance: The paper touches upon an very important topic and proposes a useful baseline solution approach. I am confident that it would be of interest to other researchers. Figure 6 is especially interesting, and I am sure it will garner a lot of attention.