Distribution Matching Variational AutoEncoder
Most visual generative models compress images into a latent space before applying diffusion or autoregressive modelling. Yet, existing approaches such as VAEs and foundation model aligned encoders implicitly constrain the latent space without explicitly shaping its distribution, making it unclear which types of distributions are optimal for modeling. We introduce Distribution-Matching VAE (DMVAE), which explicitly aligns the encoder's latent distribution with an arbitrary reference distribution via a distribution matching constraint. This generalizes beyond the Gaussian prior of conventional VAEs, enabling alignment with distributions derived from self-supervised features, diffusion noise, or other prior distributions. With DMVAE, we can systematically investigate which latent distributions are more conducive to modeling, and we find that SSL-derived distributions provide an excellent balance between reconstruction fidelity and modeling efficiency, reaching gFID equals 3.2 on ImageNet with only 64 training epochs. Our results suggest that choosing a suitable latent distribution structure (achieved via distribution-level alignment), rather than relying on fixed priors, is key to bridging the gap between easy-to-model latents and high-fidelity image synthesis. Code is avaliable at https://github.com/sen-ye/dmvae.
69379c6119d912300c34a2d0
Sen Ye
claimed_verified
2025-12-09T09:37:55.081Z
674736347f64a8dbfbf73f97
/avatars/35d825248230fe66c4ae88f153b7128e.svg
Sen Ye
sen-ye
user
sen-ye
69379c6119d912300c34a2d1
Jianning Pei
69379c6119d912300c34a2d2
Mengde Xu
69379c6119d912300c34a2d3
Shuyang Gu
69379c6119d912300c34a2d4
Chunyu Wang
69379c6119d912300c34a2d5
Liwei Wang
69379c6119d912300c34a2d6
Han Hu
Distribution Matching Variational AutoEncoder
Most visual generative models compress images into a latent space before applying diffusion or autoregressive modelling. Yet, existing approaches such as VAEs and foundation model aligned encoders implicitly constrain the latent space without explicitly shaping its distribution, making it unclear which types of distributions are optimal for modeling. We introduce Distribution-Matching VAE (DMVAE), which explicitly aligns the encoder's latent distribution with an arbitrary reference distribution via a distribution matching constraint. This generalizes beyond the Gaussian prior of conventional VAEs, enabling alignment with distributions derived from self-supervised features, diffusion noise, or other prior distributions. With DMVAE, we can systematically investigate which latent distributions are more conducive to modeling, and we find that SSL-derived distributions provide an excellent balance between reconstruction fidelity and modeling efficiency, reaching gFID equals 3.2 on ImageNet with only 64 training epochs. Our results suggest that choosing a suitable latent distribution structure (achieved via distribution-level alignment), rather than relying on fixed priors, is key to bridging the gap between easy-to-model latents and high-fidelity image synthesis. Code is avaliable at https://github.com/sen-ye/dmvae.
DMVAE explicitly aligns the encoder's latent distribution with a reference distribution, improving modeling efficiency and image synthesis fidelity compared to conventional VAEs.
VAEs foundation model aligned encoders latent space distribution matching constraint Distribution-Matching VAE (DMVAE) Gaussian prior SSL-derived distributions gFID ImageNet distribution-level alignment