Connective Viewpoints of Signal-to-Noise Diffusion Models
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866929452646137856 |
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| author | Doan, Khanh Vuong, Long Tung Nguyen, Tuan Bui, Anh Tuan Tran, Quyen Do, Thanh-Toan Phung, Dinh Le, Trung |
| author_facet | Doan, Khanh Vuong, Long Tung Nguyen, Tuan Bui, Anh Tuan Tran, Quyen Do, Thanh-Toan Phung, Dinh Le, Trung |
| contents | Diffusion models (DM) have become fundamental components of generative models, excelling across various domains such as image creation, audio generation, and complex data interpolation. Signal-to-Noise diffusion models constitute a diverse family covering most state-of-the-art diffusion models. While there have been several attempts to study Signal-to-Noise (S2N) diffusion models from various perspectives, there remains a need for a comprehensive study connecting different viewpoints and exploring new perspectives. In this study, we offer a comprehensive perspective on noise schedulers, examining their role through the lens of the signal-to-noise ratio (SNR) and its connections to information theory. Building upon this framework, we have developed a generalized backward equation to enhance the performance of the inference process. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_04221 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Connective Viewpoints of Signal-to-Noise Diffusion Models Doan, Khanh Vuong, Long Tung Nguyen, Tuan Bui, Anh Tuan Tran, Quyen Do, Thanh-Toan Phung, Dinh Le, Trung Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Neural and Evolutionary Computing Diffusion models (DM) have become fundamental components of generative models, excelling across various domains such as image creation, audio generation, and complex data interpolation. Signal-to-Noise diffusion models constitute a diverse family covering most state-of-the-art diffusion models. While there have been several attempts to study Signal-to-Noise (S2N) diffusion models from various perspectives, there remains a need for a comprehensive study connecting different viewpoints and exploring new perspectives. In this study, we offer a comprehensive perspective on noise schedulers, examining their role through the lens of the signal-to-noise ratio (SNR) and its connections to information theory. Building upon this framework, we have developed a generalized backward equation to enhance the performance of the inference process. |
| title | Connective Viewpoints of Signal-to-Noise Diffusion Models |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2408.04221 |