  This paper is a contribution towards interpretability of the deep learning
models in different applications of time-series. We propose a temporal
attention layer that is capable of selecting the relevant information to
perform various tasks, including data completion, key-frame detection and
classification. The method uses the whole input sequence to calculate an
attention value for each time step. This results in more focused attention
values and more plausible visualisation than previous methods. We apply the
proposed method to three different tasks. Experimental results show that the
proposed network produces comparable results to a state of the art. In
addition, the network provides better interpretability of the decision, that
is, it generates more significant attention weight to related frames compared
to similar techniques attempted in the past.
