Connective Viewpoints of Signal-to-Noise Diffusion Models
<|reference_start|>Connective Viewpoints of Signal-to-Noise Diffusion Models: 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.<|reference_end|>
@article{doan2024connective,
    title={Connective Viewpoints of Signal-to-Noise Diffusion Models},
    author={Khanh Doan, Long Tung Vuong, Tuan Nguyen, Anh Tuan Bui, Quyen Tran,
  Thanh-Toan Do, Dinh Phung, Trung Le},
    journal={arXiv preprint arXiv:2408.04221},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.04221},
    primaryClass={cs.CV cs.AI cs.LG cs.NE}
}