SAeUron：基于稀疏自编码器的扩散模型可解释概念遗忘
SAeUron: Interpretable Concept Unlearning in Diffusion Models with Sparse Autoencoders
AI总结：
SAeUron利用稀疏自编码器学习可解释特征，实现对扩散模型生成内容的概念遗忘，在UnlearnCanvas和I2P基准上优于现有方法，并能同时移除多概念且抵抗对抗攻击。
摘要：
扩散模型虽然功能强大，但可能无意中生成有害或不受欢迎的内容，引发重大的伦理和安全问题。最近的机器遗忘方法提供了潜在的解决方案，但往往缺乏透明度，难以理解它们对基础模型引入的变化。在这项工作中，我们引入了SAeUron，一种新颖的方法，利用稀疏自编码器（SAEs）学习到的特征来移除文本到图像扩散模型中的不需要的概念。首先，我们证明了在扩散模型的多个去噪时间步的激活上以无监督方式训练的SAEs能够捕获与特定概念相对应的稀疏且可解释的特征。在此基础上，我们提出了一种特征选择方法，能够对模型激活进行精确干预，以阻止目标内容，同时保持整体性能。我们的评估表明，SAeUron在UnlearnCanvas基准上的概念和风格遗忘任务中优于现有方法，并且在I2P评估中有效消除了色情内容。此外，我们展示了使用单个SAE可以同时移除多个概念，并且与其他方法相比，SAeUron减轻了在对抗性攻击下生成不需要内容的可能性。代码和检查点可在https://github.com/cywinski/SAeUron获取。
英文摘要：
Diffusion models, while powerful, can inadvertently generate harmful or undesirable content, raising significant ethical and safety concerns. Recent machine unlearning approaches offer potential solutions but often lack transparency, making it difficult to understand the changes they introduce to the base model. In this work, we introduce SAeUron, a novel method leveraging features learned by sparse autoencoders (SAEs) to remove unwanted concepts in text-to-image diffusion models. First, we demonstrate that SAEs, trained in an unsupervised manner on activations from multiple denoising timesteps of the diffusion model, capture sparse and interpretable features corresponding to specific concepts. Building on this, we propose a feature selection method that enables precise interventions on model activations to block targeted content while preserving overall performance. Our evaluation shows that SAeUron outperforms existing approaches on the UnlearnCanvas benchmark for concepts and style unlearning, and effectively eliminates nudity when evaluated with I2P. Moreover, we show that with a single SAE, we can remove multiple concepts simultaneously and that in contrast to other methods, SAeUron mitigates the possibility of generating unwanted content under adversarial attack. Code and checkpoints are available at https://github.com/cywinski/SAeUron.
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。
