InteL-VAEs: Adding Inductive Biases to Variational Auto-Encoders via Intermediary Latents

• URL: http://arxiv.org/abs/2106.13746v1
• Date: Fri, 25 Jun 2021 16:34:05 GMT
• Title: InteL-VAEs: Adding Inductive Biases to Variational Auto-Encoders via Intermediary Latents
• Authors: Ning Miao, Emile Mathieu, N. Siddharth, Yee Whye Teh, Tom Rainforth
• Abstract summary: We introduce a simple and effective method for learning VAEs with controllable biases by using an intermediary set of latent variables. In particular, it allows us to impose desired properties like sparsity or clustering on learned representations. We show that this, in turn, allows InteL-VAEs to learn both better generative models and representations.
• Score: 60.785317191131284
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce a simple and effective method for learning VAEs with controllable inductive biases by using an intermediary set of latent variables. This allows us to overcome the limitations of the standard Gaussian prior assumption. In particular, it allows us to impose desired properties like sparsity or clustering on learned representations, and incorporate prior information into the learned model. Our approach, which we refer to as the Intermediary Latent Space VAE (InteL-VAE), is based around controlling the stochasticity of the encoding process with the intermediary latent variables, before deterministically mapping them forward to our target latent representation, from which reconstruction is performed. This allows us to maintain all the advantages of the traditional VAE framework, while incorporating desired prior information, inductive biases, and even topological information through the latent mapping. We show that this, in turn, allows InteL-VAEs to learn both better generative models and representations.

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