Variational Lossy Autoencoder
ICLR, Volume abs/1611.02731, 2017.
VLAE has the appealing properties of controllable representation learning and improved density estimation performance but these properties come at a cost: compared with variational autoencoder models that have simple prior and decoder, VLAE is slower at generation due to the sequ...
Representation learning seeks to expose certain aspects of observed data in a learned representation that's amenable to downstream tasks like classification. For instance, a good representation for 2D images might be one that describes only global structure and discards information about detailed texture. In this paper, we present a sim...More
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