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arXiv · 1412.6257

Gradual training of deep denoising auto encoders

Alexander Kalmanovich·Gal Chechik
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Abstract

Stacked denoising auto encoders (DAEs) are well known to learn useful deep representations, which can be used to improve supervised training by initializing a deep network. We investigate a training scheme of a deep DAE, where DAE layers are gradually added and keep adapting as additional layers are added. We show that in the regime of mid-sized datasets, this gradual training provides a small but consistent improvement over stacked training in both reconstruction quality and classification error over stacked training on MNIST and CIFAR datasets.

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Alexander Kalmanovich, Gal Chechik. 2014-12-19. Gradual training of deep denoising auto encoders. https://arxiv.org/abs/1412.6257

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