  We propose split-brain autoencoders, a straightforward modification of the
traditional 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 to other, unseen tasks. This method achieves
state-of-the-art performance on several large-scale transfer learning
benchmarks.
