Sparse Auto Encoder

Sparse Auto Encoder

SAE is one approach to automatically learn features from unlabeled data. Instead of comparing the inputs and outputs, the network feeds back the input plus a sparsity driver. The driver acts like a threshold of error- resembling a spiking neural network- and allows the average activation value to be small.

See Andrew Ng’s lecture on SAE at Stanford

Leave a Reply

Your email address will not be published. Required fields are marked *