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

Richard Zhang; Phillip Isola; Alexei A. Efros

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arXiv · 1611.09842

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

Richard Zhang·Phillip Isola·Alexei A. Efros

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Abstract

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.

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BibTeX RIS

Richard Zhang, Phillip Isola, Alexei A. Efros. 2016-11-29. Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction. https://arxiv.org/abs/1611.09842

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