Denoising Multi-Beta VAE: Representation Learning for Disentanglement and Generation
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866912472953257984 |
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| author | Uppal, Anshuk Takida, Yuhta Lai, Chieh-Hsin Mitsufuji, Yuki |
| author_facet | Uppal, Anshuk Takida, Yuhta Lai, Chieh-Hsin Mitsufuji, Yuki |
| contents | Disentangled and interpretable latent representations in generative models typically come at the cost of generation quality. The $β$-VAE framework introduces a hyperparameter $β$ to balance disentanglement and reconstruction quality, where setting $β> 1$ 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 $β$ 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 $β$ value prioritize disentanglement over reconstruction fidelity. We then, introduce a non-linear diffusion model that smoothly transitions latent representations corresponding to different $β$ 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 $β$, facilitating consistent manipulation of generated outputs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_06613 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Denoising Multi-Beta VAE: Representation Learning for Disentanglement and Generation Uppal, Anshuk Takida, Yuhta Lai, Chieh-Hsin Mitsufuji, Yuki Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition Disentangled and interpretable latent representations in generative models typically come at the cost of generation quality. The $β$-VAE framework introduces a hyperparameter $β$ to balance disentanglement and reconstruction quality, where setting $β> 1$ 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 $β$ 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 $β$ value prioritize disentanglement over reconstruction fidelity. We then, introduce a non-linear diffusion model that smoothly transitions latent representations corresponding to different $β$ 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 $β$, facilitating consistent manipulation of generated outputs. |
| title | Denoising Multi-Beta VAE: Representation Learning for Disentanglement and Generation |
| topic | Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.06613 |