Denoising Multi-Beta VAE: Representation Learning for Disentanglement and Generation

• URL: http://arxiv.org/abs/2507.06613v1
• Date: Wed, 09 Jul 2025 07:29:41 GMT
• Title: Denoising Multi-Beta VAE: Representation Learning for Disentanglement and Generation
• Authors: Anshuk Uppal, Yuhta Takida, Chieh-Hsin Lai, Yuki Mitsufuji,
• Abstract summary: We propose a novel generative modeling framework that leverages a range of $beta$ values to learn multiple corresponding latent representations.<n>We introduce a non-linear diffusion model that smoothly transitions latent representations corresponding to different $beta$ values.<n>We evaluate our framework in terms of both disentanglement and generation quality.
• Score: 15.957980475573365
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Disentangled and interpretable latent representations in generative models typically come at the cost of generation quality. The $\beta$-VAE framework introduces a hyperparameter $\beta$ to balance disentanglement and reconstruction quality, where setting $\beta > 1$ introduces an information bottleneck that favors disentanglement over sharp, accurate reconstructions. To address this trade-off, we propose a novel generative modeling framework that leverages a range of $\beta$ values to learn multiple corresponding latent representations. First, we obtain a slew of representations by training a single variational autoencoder (VAE), with a new loss function that controls the information retained in each latent representation such that the higher $\beta$ value prioritize disentanglement over reconstruction fidelity. We then, introduce a non-linear diffusion model that smoothly transitions latent representations corresponding to different $\beta$ values. This model denoises towards less disentangled and more informative representations, ultimately leading to (almost) lossless representations, enabling sharp reconstructions. Furthermore, our model supports sample generation without input images, functioning as a standalone generative model. We evaluate our framework in terms of both disentanglement and generation quality. Additionally, we observe smooth transitions in the latent spaces with respect to changes in $\beta$, facilitating consistent manipulation of generated outputs.

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