Review -- Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction

By performing this pretext task of predicting X2 from X1, we hope to achieve a representation F(X1) which contains high-level abstractions or semantics. By concatenating the representations layer-wise, Fl {Fl1, Fl2}, a representation F is achieved which is pretrained on full input tensor X. If F is a CNN of a desired fixed size, e.g., AlexNet, we can design the subnetworks F1, F2 by splitting each layer of the network F in half, along the channel dimension. However, it is found that the proposed Split-Brain Auto (Section 1.2) outperforms the above two alternatives (Section 1.3).