Efficient Learning of Sparse Invariant Representations

Understand

We propose a simple and efficient algorithm for learning sparse invariant representations from unlabeled data with fast inference.

• When trained on short movies sequences, the learned features are selective to a range of orientations and spatial frequencies, but robust to a wide range of positions, similar to complex cells in the primary visual cortex.
• We give a hierarchical version of the algorithm, and give guarantees of fast convergence under certain conditions.