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Title: Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction
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.  more » « less
Award ID(s):
1633310 1514512
PAR ID:
10040279
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN:
2332-564X
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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