SC-VAE: Sparse Coding-based Variational Autoencoder with Learned ISTA

Learning rich data representations from unlabeled data is a key challenge towards applying deep learning algorithms in downstream tasks. The proposed method learns sparse data representations that consist of a linear combination of a small number of predetermined orthogonal atoms.