autoencoder architecture, for unsupervised representation learning. The method adds a split
to the network, resulting in two disjoint sub-networks. Each sub-network is trained to perform
a difficult task--predicting one subset of the data channels from another. Together, the sub-
networks extract features from the entire input signal. By forcing the network to solve cross-
channel prediction tasks, we induce a representation within the network which transfers well …