NoiseCLR：一种用于无监督发现扩散模型中可解释方向的对比学习方法
NoiseCLR: A Contrastive Learning Approach for Unsupervised Discovery of Interpretable Directions in Diffusion Models
AI总结：
针对文本提示难以覆盖专业领域语义的问题，本文提出 NoiseCLR，利用少量未标记图像和对比学习无监督发现文本到图像扩散模型中的可解释方向，并支持多方向、跨域同时编辑。
摘要：
近年来，生成模型因其图像生成能力而广受欢迎。基于 GAN 的模型因其解耦的潜空间而受到高度重视，这是促成其在受控图像编辑中取得成功的关键特征。另一方面，扩散模型已成为生成高质量图像的强大工具。然而，扩散模型的潜空间尚未得到如此充分的探索或理解。旨在探索扩散模型潜空间的现有方法通常依赖文本提示来确定特定语义。然而，这种方法在艺术、时尚或医学等专业领域可能存在局限，因为在这些领域可能没有合适的文本提示可用，或难以构思合适的提示，从而限制了现有工作的范围。本文提出一种无监督方法，用于在不依赖文本提示的情况下发现文本到图像扩散模型中的潜语义。我们的方法接收来自特定领域（例如人脸或猫）的一小组未标记图像以及一个预训练扩散模型，并使用对比学习目标以无监督方式发现多样化语义。此外，学习到的方向可以同时应用，无论是在同一领域内（例如各种类型的人脸编辑），还是跨不同领域（例如在同一张图像中应用猫和人脸编辑），彼此之间都不会相互干扰。大量实验表明，我们的方法实现了高度解耦的编辑，在基于扩散和基于 GAN 的潜空间编辑方法方面均优于现有方法。
英文摘要：
Generative models have been very popular in the recent years for their image generation capabilities. GAN-based models are highly regarded for their disentangled latent space, which is a key feature contributing to their success in controlled image editing. On the other hand, diffusion models have emerged as powerful tools for generating high-quality images. However, the latent space of diffusion models is not as thoroughly explored or understood. Existing methods that aim to explore the latent space of diffusion models usually relies on text prompts to pinpoint specific semantics. However, this approach may be restrictive in areas such as art, fashion, or specialized fields like medicine, where suitable text prompts might not be available or easy to conceive thus limiting the scope of existing work. In this paper, we propose an unsupervised method to discover latent semantics in text-to-image diffusion models without relying on text prompts. Our method takes a small set of unlabeled images from specific domains, such as faces or cats, and a pre-trained diffusion model, and discovers diverse semantics in unsupervised fashion using a contrastive learning objective. Moreover, the learned directions can be applied simultaneously, either within the same domain (such as various types of facial edits) or across different domains (such as applying cat and face edits within the same image) without interfering with each other. Our extensive experiments show that our method achieves highly disentangled edits, outperforming existing approaches in both diffusion-based and GAN-based latent space editing methods.
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。
