Sparse Autoencoder (SAE)

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Sparse autoencoders (SAE) are in a way the opposite of AEs. Instead of teaching a network to represent a bunch of information in less “space” or nodes, we try to encode information in more space. So instead of the network converging in the middle and then expanding back to the input size, we blow up the middle. These types of networks can be used to extract many small features from a dataset. If one were to train a SAE the same way as an AE, you would in almost all cases end up with a pretty useless identity network (as in what comes in is what comes out, without any transformation or decomposition). To prevent this, instead of feeding back the input, we feed back the input plus a sparsity driver. This sparsity driver can take the form of a threshold filter, where only a certain error is passed back and trained, the other error will be “irrelevant” for that pass and set to zero. In a way this resembles spiking neural networks, where not all neurons fire all the time (and points are scored for biological plausibility). Marc’Aurelio Ranzato, Christopher Poultney, Sumit Chopra, and Yann LeCun. “Efficient learning of sparse representations with an energy-based model.” Proceedings of NIPS. 2007.