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This paper is a contribution towards interpretability of the deep learningmodels in different applications of time-series. We propose a temporalattention layer that is capable of selecting the relevant information toperform various tasks, including data completion, key-frame detection andclassification. The method uses the whole input sequence to calculate anattention value for each time step. This results in more focused attentionvalues and more plausible visualisation than previous methods. We apply theproposed method to three different tasks. Experimental results show that theproposed network produces comparable results to a state of the art. Inaddition, the network provides better interpretability of the decision, thatis, it generates more significant attention weight to related frames comparedto similar techniques attempted in the past.

Focusing on What is Relevant: Time-Series Learning and Understanding using Attention