GCVAE: Generalized-Controllable Variational AutoEncoder

• URL: http://arxiv.org/abs/2206.04225v1
• Date: Thu, 9 Jun 2022 02:29:30 GMT
• Title: GCVAE: Generalized-Controllable Variational AutoEncoder
• Authors: Kenneth Ezukwoke, Anis Hoayek, Mireille Batton-Hubert, and Xavier Boucher
• Abstract summary: We present a framework to handle the trade-off between attaining extremely low reconstruction error and a high disentanglement score. We prove that maximizing information in the reconstruction network is equivalent to information during amortized inference.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Variational autoencoders (VAEs) have recently been used for unsupervised disentanglement learning of complex density distributions. Numerous variants exist to encourage disentanglement in latent space while improving reconstruction. However, none have simultaneously managed the trade-off between attaining extremely low reconstruction error and a high disentanglement score. We present a generalized framework to handle this challenge under constrained optimization and demonstrate that it outperforms state-of-the-art existing models as regards disentanglement while balancing reconstruction. We introduce three controllable Lagrangian hyperparameters to control reconstruction loss, KL divergence loss and correlation measure. We prove that maximizing information in the reconstruction network is equivalent to information maximization during amortized inference under reasonable assumptions and constraint relaxation.

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