DGAE: Diffusion-Guided Autoencoder for Efficient Latent Representation Learning
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911373293780992 |
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| author | Liu, Dongxu Zhu, Jiahui Peng, Yuang Tang, Haomiao Chen, Yuwei Han, Chunrui Ge, Zheng Jiang, Daxin Liao, Mingxue |
| author_facet | Liu, Dongxu Zhu, Jiahui Peng, Yuang Tang, Haomiao Chen, Yuwei Han, Chunrui Ge, Zheng Jiang, Daxin Liao, Mingxue |
| contents | Autoencoders empower state-of-the-art image and video generative models by compressing pixels into a latent space through visual tokenization. Although recent advances have alleviated the performance degradation of autoencoders under high compression ratios, addressing the training instability caused by GAN remains an open challenge. While improving spatial compression, we also aim to minimize the latent space dimensionality, enabling more efficient and compact representations. To tackle these challenges, we focus on improving the decoder's expressiveness. Concretely, we propose DGAE, which employs a diffusion model to guide the decoder in recovering informative signals that are not fully decoded from the latent representation. With this design, DGAE effectively mitigates the performance degradation under high spatial compression rates. At the same time, DGAE achieves state-of-the-art performance with a 2x smaller latent space. When integrated with Diffusion Models, DGAE demonstrates competitive performance on image generation for ImageNet-1K and shows that this compact latent representation facilitates faster convergence of the diffusion model. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_09644 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DGAE: Diffusion-Guided Autoencoder for Efficient Latent Representation Learning Liu, Dongxu Zhu, Jiahui Peng, Yuang Tang, Haomiao Chen, Yuwei Han, Chunrui Ge, Zheng Jiang, Daxin Liao, Mingxue Computer Vision and Pattern Recognition Artificial Intelligence Autoencoders empower state-of-the-art image and video generative models by compressing pixels into a latent space through visual tokenization. Although recent advances have alleviated the performance degradation of autoencoders under high compression ratios, addressing the training instability caused by GAN remains an open challenge. While improving spatial compression, we also aim to minimize the latent space dimensionality, enabling more efficient and compact representations. To tackle these challenges, we focus on improving the decoder's expressiveness. Concretely, we propose DGAE, which employs a diffusion model to guide the decoder in recovering informative signals that are not fully decoded from the latent representation. With this design, DGAE effectively mitigates the performance degradation under high spatial compression rates. At the same time, DGAE achieves state-of-the-art performance with a 2x smaller latent space. When integrated with Diffusion Models, DGAE demonstrates competitive performance on image generation for ImageNet-1K and shows that this compact latent representation facilitates faster convergence of the diffusion model. |
| title | DGAE: Diffusion-Guided Autoencoder for Efficient Latent Representation Learning |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2506.09644 |