An exploration and analysis of multistage convolutional architectures for object recognition

An exploration and analysis of multistage convolutional architectures for object recognition, 2013.

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Abstract:

In this thesis we study various architectures and training procedures to learn sparse convolutional feature hierarchies. We start with a modified form of classical sparse coding that learns a feed-forward function to predict the optimal sparse codes. This function can then be integrated into a globally trained multistage architecture. We ...More

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