扩散模型通过主动破坏数据来训练生成，更适应数据稀缺场景。
Destruction is a General Strategy to Learn Generation; Diffusion's Strength is to Take it Seriously; Exploration is the Future
- 用破坏数据的方式训练模型还原原始信息，比传统方法更灵活。
- 在数据少的场景下，该策略表现更优，验证了其有效性。
- 提出新概率图模型辅助理解，适合研究生成模型机制者阅读。
我将扩散模型归为一类机器学习技术，其核心是向模型输入中隐藏信息，并训练模型推测被隐藏的内容。我认为扩散模型采用的破坏性信息隐藏方式，相比传统的手工设计方法更具灵活性，能在特定场景（尤其是数据稀缺时）提供更丰富的训练空间。随后，我探讨了将强化学习技术迁移到扩散框架时可能遇到的微妙问题，并思考如何以更符合扩散本质的方式解决探索难题。虽无确定答案，但指出了若干值得探索的方向。论文后附教程，深化‘先破坏再生成’的视角，并引入一种新型概率图模型以辅助阐述。
原文摘要 · Abstract (English)
I present diffusion models as part of a family of machine learning techniques that withhold information from a model's input and train it to guess the withheld information. I argue that diffusion's destroying approach to withholding is more flexible than typical hand-crafted information withholding techniques, providing a rich training playground that could be advantageous in some settings, notably data-scarce ones. I then address subtle issues that may arise when porting reinforcement learning techniques to the diffusion context, and wonder how such exploration problems could be addressed in more diffusion-native ways. I do not have definitive answers, but I do point my fingers in directions I deem interesting. A tutorial follows this thesis, expanding on the destroy-then-generate perspective. A novel kind of probabilistic graphical models is introduced to facilitate the tutorial's exposition.
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