Exploiting Spatio-Temporal Structure With Recurrent Winner-Take-All Networks
Exploiting Spatio-Temporal Structure With Recurrent Winner-Take-All Networks
We propose a convolutional recurrent neural network (ConvRNNs), with winner-take-all (WTA) dropout for high-dimensional unsupervised feature learning in multidimensional time series. We apply the proposed method for object recognition using temporal context in videos and obtain better results than comparable methods in the literature, including the deep predictive coding networks …