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Analysis of Video Feature Learning in Two-Stream CNNs on the Example of Zebrafish Swim Bout Classification

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Original languageEnglish
Title of host publicationProceedings of the International Conference on Learning Representations, 2020
Place of PublicationAddis Ababa, Ethiopia
Publication statusE-pub ahead of print - 20 Dec 2019
EventEighth International Conference on Learning Representations - Millennium Hall, Addis Ababa, Ethiopia
Duration: 26 Apr 202030 Apr 2020
https://iclr.cc/Conferences/2020

Conference

ConferenceEighth International Conference on Learning Representations
Abbreviated titleICLR 2020
CountryEthiopia
CityAddis Ababa
Period26/04/2030/04/20
Internet address

Abstract

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.

    Research areas

  • cs.CV, cs.LG, eess.IV

Event

Eighth International Conference on Learning Representations

26/04/2030/04/20

Addis Ababa, Ethiopia

Event: Conference

ID: 131000066