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This paper addresses the problem of image compression using sparse representations. We propose a variant of autoencoder called Stochastic Winner-Take-All Auto-Encoder (SWTA AE). “Winner-Take-All” means that image patches compete with one another when computing their sparse representation and “Stochastic” indicates that a stochastic hyperparameter rules this competition during training. Unlike auto-encoders,...
This paper considers the problem of image compression with shallow sparse autoencoders. We use both a T-sparse autoencoder (T-sparse AE) and a winner-take-all autoencoder (WTA AE). A performance analysis in terms of rate-distortion trade-off and complexity is conducted, comparing with LARS-Lasso, Coordinate Descent (CoD) and Orthogonal Matching Pursuit (OMP). We show that, WTA AE achieves the best...
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