This notebook tries to reproduce the learning strategy specified in the framework of the SparseNet algorithm from Bruno Olshausen. It allows to efficiently code natural image patches by constraining the code to be sparse.
the underlying machinery uses a similar dictionary learning as used in the image denoising example from
sklearnand our aim here is to show that a novel ingredient is necessary to reproduce Olshausen's results.
All these code bits is regrouped in the SHL scripts repository (where you will also find some older matlab code). You may install it using
pip install git+https://github.com/bicv/SHL_scripts
This is joint work with Victor Boutin.