  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.
