Stacked Convolutional Auto-Encoders for Hierarchical Feature Extraction Stacked Convolutional Auto-Encoders for Hierarchical Feature Extraction
Paper summary _Objective:_ Introduces the Convolutional Auto-Encoder, a hierarchical unsupervised feature extractor. _Dataset:_ [MNIST](yann.lecun.com/exdb/mnist/) and [SVHN](ufldl.stanford.edu/housenumbers/). #### Architecture: Uses convolutions to generate an encoding of the image and then decodes it and do a pixel-wise comparison. Used to initializes CNN. #### Results: Old article, not really relevant nowadays. They don't speak about the deconvolution part.

Summary by Léo Paillier 3 months ago
Loading...
Your comment:


ShortScience.org allows researchers to publish paper summaries that are voted on and ranked!
About

Sponsored by: and