Disentangled Representation Learning with Sequential Residual Variational Autoencoder

ICLR 2020 Nanxiang LiShabnam GhaffarzadeganLiu Ren

Recent advancements in unsupervised disentangled representation learning focus on extending the variational autoencoder (VAE) with an augmented objective function to balance the trade-off between disentanglement and reconstruction. We propose Sequential Residual Variational Autoencoder (SR-VAE) that defines a "Residual learning" mechanism as the training regime instead of the augmented objective function... (read more)

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