Explain about Sparse Autoencoder?
Ans: In sparse autoencoders with a sparsity enforcer that directs a single layer network to learn code dictionary which minimizes the error in reproducing the input while constraining number of code words for reconstruction.
The sparse autoencoder consists of a single hidden layer, which is connected to the input vector by a weight matrix forming the encoding step. The hidden layer outputs to a reconstruction vector, using a tied weight matrix to form the decoder.
Sparse autoencoders are used to learn features from another task, such as classiﬁcation. A regularized autoencoder to be sparse must respond to unique statistical features of the trained dataset, instead of simply acting as an identity function.