Analysis of Video Feature Learning in Two-Stream CNNs on the Example of Zebrafish Swim Bout Classification
Semmelhack et al. (2014) have achieved high classification accuracy in distinguishing swim bouts of zebrafish using a Support Vector Machine (SVM). Convolutional Neural Networks (CNNs) have reached superior performance in various image recognition tasks over SVMs, but these powerful networks remain a black box. Reaching better transparency helps to build trust in their classifications and makes learned features interpretable to experts. Using a recently developed technique called Deep Taylor Decomposition, we generated heatmaps to highlight input regions of high relevance for predictions. We find that our CNN makes predictions by analyzing the steadiness of the tail*s trunk, which markedly differs from the manually extracted features used by Semmelhack et al. (2014). We further uncovered that the network paid attention to experimental artifacts. Removing these artifacts ensured the validity of predictions. After correction, our best CNN beats the SVM by 6.12%, achieving a classification accuracy of 96.32%. Our work thus demonstrates the utility of AI explainability for CNNs.
The paper presents a case study of training a video classifier using convolutional networks, and on how the learned features related to previous, hand-designed ones. The particular domain considered is of importance for biologists/medical doctors/neuroscientists: zebra fish swim bout classification. In order to identify which particular features the neural networks are paying attention to, the paper used Deep Taylor Decomposition, which allowed the authors to identify *clever-hans*-type phenomena (network attending to meaningless experimental setup differences that actually gave a way the ground truth classes). This allowed for the authors to mask out such features and make the network attend to more meaningful ones. In particular observations like * looking for salient features in the trunk of the tail while largely disregarding the tip*, are typically absent from most deep learning studies and it*s quite interesting. Overall the paper is well written, the experiments are well designed; everything seems very rigorous and well executed. It makes for a very good quality practitioner-level case study on video understanding, which may also be useful for people studying zebra fish or related simple life forms. My main concern with the paper is whether ICLR is an appropriate venue, as it does not provide pure machine learning contributions in the form of new techniques of generally applicable insights.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Analysis of Video Feature Learning in Two-Stream CNNs on the Example of Zebrafish Swim Bout Classification
Semmelhack et al. (2014) have achieved high classification accuracy in distinguishing swim bouts of zebrafish using a Support Vector Machine (SVM). Convolutional Neural Networks (CNNs) have reached superior performance in various image recognition tasks over SVMs, but these powerful networks remain a black box. Reaching better transparency helps to build trust in their classifications and makes learned features interpretable to experts. Using a recently developed technique called Deep Taylor Decomposition, we generated heatmaps to highlight input regions of high relevance for predictions. We find that our CNN makes predictions by analyzing the steadiness of the tail*s trunk, which markedly differs from the manually extracted features used by Semmelhack et al. (2014). We further uncovered that the network paid attention to experimental artifacts. Removing these artifacts ensured the validity of predictions. After correction, our best CNN beats the SVM by 6.12%, achieving a classification accuracy of 96.32%. Our work thus demonstrates the utility of AI explainability for CNNs.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



The paper presents a case study of training a video classifier using convolutional networks, and on how the learned features related to previous, hand-designed ones. The particular domain considered is of importance for biologists/medical doctors/neuroscientists: zebra fish swim bout classification. In order to identify which particular features the neural networks are paying attention to, the paper used Deep Taylor Decomposition, which allowed the authors to identify *clever-hans*-type phenomena (network attending to meaningless experimental setup differences that actually gave a way the ground truth classes). This allowed for the authors to mask out such features and make the network attend to more meaningful ones. In particular observations like * looking for salient features in the trunk of the tail while largely disregarding the tip*, are typically absent from most deep learning studies and it*s quite interesting. Overall the paper is well written, the experiments are well designed; everything seems very rigorous and well executed. It makes for a very good quality practitioner-level case study on video understanding, which may also be useful for people studying zebra fish or related simple life forms. My main concern with the paper is whether ICLR is an appropriate venue, as it does not provide pure machine learning contributions in the form of new techniques of generally applicable insights.
assistant
The paper presents a case study of training a video classifier using convolutional networks, and on how the learned features related to previous, hand-designed ones. The particular domain considered is of importance for biologists/medical doctors/neuroscientists: zebra fish swim bout classification. In order to identify which particular features the neural networks are paying attention to, the paper used Deep Taylor Decomposition, which allowed the authors to identify *clever-hans*-type phenomena (network attending to meaningless experimental setup differences that actually gave a way the ground truth classes). This allowed for the authors to mask out such features and make the network attend to more meaningful ones. In particular observations like * looking for salient features in the trunk of the tail while largely disregarding the tip*, are typically absent from most deep learning studies and it*s quite interesting. Overall the paper is well written, the experiments are well designed; everything seems very rigorous and well executed. It makes for a very good quality practitioner-level case study on video understanding, which may also be useful for people studying zebra fish or related simple life forms. My main concern with the paper is whether ICLR is an appropriate venue, as it does not provide pure machine learning contributions in the form of new techniques of generally applicable insights.