Disentanglement with Factor Quantized Variational Autoencoders

• URL: http://arxiv.org/abs/2409.14851v1
• Date: Mon, 23 Sep 2024 09:33:53 GMT
• Title: Disentanglement with Factor Quantized Variational Autoencoders
• Authors: Gulcin Baykal, Melih Kandemir, Gozde Unal,
• Abstract summary: We propose a discrete variational autoencoder (VAE) based model where the ground truth information about the generative factors are not provided to the model. We demonstrate the advantages of learning discrete representations over learning continuous representations in facilitating disentanglement. Our method called FactorQVAE is the first method that combines optimization based disentanglement approaches with discrete representation learning.
• Score: 11.086500036180222
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Disentangled representation learning aims to represent the underlying generative factors of a dataset in a latent representation independently of one another. In our work, we propose a discrete variational autoencoder (VAE) based model where the ground truth information about the generative factors are not provided to the model. We demonstrate the advantages of learning discrete representations over learning continuous representations in facilitating disentanglement. Furthermore, we propose incorporating an inductive bias into the model to further enhance disentanglement. Precisely, we propose scalar quantization of the latent variables in a latent representation with scalar values from a global codebook, and we add a total correlation term to the optimization as an inductive bias. Our method called FactorQVAE is the first method that combines optimization based disentanglement approaches with discrete representation learning, and it outperforms the former disentanglement methods in terms of two disentanglement metrics (DCI and InfoMEC) while improving the reconstruction performance. Our code can be found at \url{https://github.com/ituvisionlab/FactorQVAE}.

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