Papers › Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction
Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction
Richard Zhang, Phillip Isola, Alexei A. Efros
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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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Self-Supervised Image Classification | ImageNet | Split-Brain (AlexNet) | Number of Params | 61M | #141 of 144 | Archive leaderboard | report |
| Self-Supervised Image Classification | ImageNet | Split-Brain (AlexNet) | Top 1 Accuracy | 35.4% | #141 of 144 | Archive leaderboard | report |
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