Relaxed-Responsibility Hierarchical Discrete VAEs

• URL: http://arxiv.org/abs/2007.07307v2
• Date: Thu, 4 Feb 2021 18:59:59 GMT
• Title: Relaxed-Responsibility Hierarchical Discrete VAEs
• Authors: Matthew Willetts, Xenia Miscouridou, Stephen Roberts, Chris Holmes
• Abstract summary: We introduce textitRelaxed-Responsibility Vector-Quantisation, a novel way to parameterise discrete latent variables. We achieve state-of-the-art bits-per-dim results for various standard datasets.
• Score: 3.976291254896486
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Successfully training Variational Autoencoders (VAEs) with a hierarchy of discrete latent variables remains an area of active research. Vector-Quantised VAEs are a powerful approach to discrete VAEs, but naive hierarchical extensions can be unstable when training. Leveraging insights from classical methods of inference we introduce \textit{Relaxed-Responsibility Vector-Quantisation}, a novel way to parameterise discrete latent variables, a refinement of relaxed Vector-Quantisation that gives better performance and more stable training. This enables a novel approach to hierarchical discrete variational autoencoders with numerous layers of latent variables (here up to 32) that we train end-to-end. Within hierarchical probabilistic deep generative models with discrete latent variables trained end-to-end, we achieve state-of-the-art bits-per-dim results for various standard datasets. % Unlike discrete VAEs with a single layer of latent variables, we can produce samples by ancestral sampling: it is not essential to train a second autoregressive generative model over the learnt latent representations to then sample from and then decode. % Moreover, that latter approach in these deep hierarchical models would require thousands of forward passes to generate a single sample. Further, we observe different layers of our model become associated with different aspects of the data.

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