pipeline_tag: unconditional-image-generation
Distribution Matching Variational AutoEncoder (DMVAE)
This repository contains the official implementation of the paper "Distribution Matching Variational AutoEncoder".
DMVAE introduces a novel approach to visual generative models by explicitly aligning the encoder's latent distribution with an arbitrary reference distribution via a distribution matching constraint. This method generalizes beyond the Gaussian prior of conventional VAEs, enabling alignment with distributions derived from self-supervised features, diffusion noise, or other prior distributions, leading to efficient and high-fidelity image synthesis.
For more details on the installation, training, and evaluation, please refer to the official GitHub repository.
Citation
If you find this repository useful in your research or applications, please consider citing:
@misc{dmvae,
title={Distribution Matching Variational AutoEncoder},
author={Sen Ye and Jianning Pei and Mengde Xu and Shuyang Gu and Chunyu Wang and Liwei Wang and Han Hu},
year={2025},
eprint={2512.07778},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2512.07778},
}