Generative Models

# Sparse Autoencoder

A Sparse Autoencoder is a type of autoencoder that employs sparsity to achieve an information bottleneck. Specifically the loss function is constructed so that activations are penalized within a layer. The sparsity constraint can be imposed with L1 regularization or a KL divergence between expected average neuron activation to an ideal distribution $p$.

Image: Jeff Jordan. Read his blog post (click) for a detailed summary of autoencoders.

#### Papers

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