Contractive Auto-Encoders: Explicit Invariance During Feature Extraction

Abstract

We present in this paper a novel approach for training deterministic auto-encoders. We show that by adding a well chosen penalty term to the classical reconstruction cost function, we can achieve results that equal or surpass those attained by other regularized autoencoders as well as denoising auto-encoders on a range of datasets. This penalty term… (More)

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