Patronus：基于原型的可解释扩散模型
Patronus: Interpretable Diffusion Models with Prototypes
AI总结：
Patronus通过原型网络实现可解释的扩散模型，揭示生成过程中的视觉模式及语义演变，提升对扩散模型的理解与控制。
摘要：
揭示基于扩散的生成模型的不透明性至关重要，因为其应用不断扩大，而其底层过程大多仍是一个黑箱。针对关键问题——如何解释和理解扩散生成过程？——我们提出了Patronus，一种可解释的扩散模型，该模型结合了原型网络来在视觉块中编码语义，揭示了哪些视觉模式被建模以及它们在去噪过程中何时何地出现。Patronus的可解释性提供了对生成机制的深入理解，使能够通过不希望的相关性检测捷径学习，并在时间步中追踪语义的出现。我们在四个自然图像数据集和一个医学成像数据集上评估了Patronus，展示了忠实的可解释性和强大的生成性能。通过这项工作，我们通过基于原型的可解释性开辟了理解和引导扩散模型的新途径。我们的代码可在https://github.com/nina-weng/patronus获取。
英文摘要：
Uncovering the opacity of diffusion-based generative models is urgently needed, as their applications continue to expand while their underlying procedures largely remain a black box. With a critical question -- how can the diffusion generation process be interpreted and understood? -- we proposed Patronus, an interpretable diffusion model that incorporates a prototypical network to encode semantics in visual patches, revealing what visual patterns are modeled and where and when they emerge throughout denoising. This interpretability of Patronus provides deeper insights into the generative mechanism, enabling the detection of shortcut learning via unwanted correlations and the tracing of semantic emergence across timesteps. We evaluate Patronus on four natural image datasets and one medical imaging dataset, demonstrating both faithful interpretability and strong generative performance. With this work, we open new avenues for understanding and steering diffusion models through prototype-based interpretability.\\ Our code is available at https://github.com/nina-weng/patronus}{https://github.com/nina-weng/patronus.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
