A computer-implemented method for representation disentanglement is provided. The method includes encoding an input vector into an embedding. The method further includes learning, by a hardware processor, disentangled representations of the input vector including a style embedding and a content embedding by performing sample-based mutual information minimization on the embedding under a Wasserstein distance regularization and a Kullback-Leibler (KL) divergence. The method also includes decoding the style and content embeddings to obtain a reconstructed vector.
INFORMATION THEORY GUIDED SEQUENTIAL REPRESENTATION DISENTANGLEMENT AND DATA GENERATION