Recent advances in autoencoder-based representation learning


Michael Tschannen, Olivier Bachem, and Mario Lucic


Third workshop on Bayesian Deep Learning (NeurIPS 2018), 2018.

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Learning useful representations with little or no supervision is a key challenge in artificial intelligence. We provide an in-depth review of recent advances in representation learning with a focus on autoencoder-based models. To organize these results we make use of meta-priors believed useful for downstream tasks, such as disentanglement and hierarchical organization of features. In particular, we un- cover three main mechanisms to enforce such properties, namely (i) regularizing the (approximate or aggregate) posterior distribution, (ii) factorizing the encoding and decoding distribution, or (iii) introducing a structured prior distribution. While there are some promising results, implicit or explicit supervision remains a key enabler and all current methods use strong inductive biases and modeling assumptions. Finally, we provide an analysis of autoencoder-based representation learning through the lens of rate-distortion theory and identify a clear tradeoff between the amount of prior knowledge available about the downstream tasks, and how useful the representation is for this task.


autoencoder, variational autoencoder, representation learning, disentanglement, rate-distortion tradeoff, unsupervised

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Copyright Notice: © 2018 M. Tschannen, O. Bachem, and M. Lucic.

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