|
|
--- |
|
|
pipeline_tag: unconditional-image-generation |
|
|
--- |
|
|
|
|
|
# Distribution Matching Variational AutoEncoder (DMVAE) |
|
|
|
|
|
This repository contains the official implementation of the paper [**"Distribution Matching Variational AutoEncoder"**](https://huggingface.co/papers/2512.07778). |
|
|
|
|
|
 |
|
|
|
|
|
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](https://github.com/sen-ye/dmvae). |
|
|
|
|
|
## Citation |
|
|
|
|
|
If you find this repository useful in your research or applications, please consider citing: |
|
|
|
|
|
```text |
|
|
@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}, |
|
|
} |
|
|
``` |
