While many unsupervised learning models focus on one family of tasks, either generative or discriminative, we explore the possibility of a unified representation learner: a model which addresses both families of tasks simultaneously. We identify diffusion models, a state-of-the-art method for generative tasks, as a prime candidate. Such models involve training a U-Net to iteratively predict and remove noise, and the resulting model can synthesize high-fidelity, diverse, novel images. We find that the intermediate feature maps of the U-Net are diverse, discriminative feature representations. We propose a novel attention mechanism for pooling feature maps and further leverage this mechanism as DifFormer, a transformer feature fusion of features from different diffusion U-Net blocks and noise steps. We also develop DifFeed, a novel feedback mechanism tailored to diffusion. We find that diffusion models are better than GANs, and, with our fusion and feedback mechanisms, can compete with state-of-the-art unsupervised image representation learning methods for discriminative tasks - image classification with full and semi-supervision, transfer for fine-grained classification, object detection and segmentation, and semantic segmentation. Our project website (https://mgwillia.github.io/diffssl/) and code (https://github.com/soumik-kanad/diffssl) are available publicly.

翻译：虽然许多无监督学习模型专注于单一任务族（生成式或判别式），但我们探索了统一表征学习器的可能性——即能同时处理两类任务的模型。我们认定扩散模型（生成任务的最先进方法）是首要候选。这类模型需要训练一个U-Net迭代预测并去除噪声，最终模型能合成高保真度、多样化的新图像。我们发现U-Net的中间特征图本身即为多样化的判别性特征表征。我们提出一种新颖的池化特征图注意力机制，并将其发展为DifFormer——一种融合不同扩散U-Net模块与噪声步特征的Transformer特征融合器。我们还开发了专门适配扩散模型的反馈机制DifFeed。实验表明，扩散模型优于GANs，且通过我们的融合与反馈机制，能在判别性任务（全监督与半监督图像分类、细粒度分类迁移、目标检测分割、语义分割）上媲美最先进的无监督图像表征学习方法。我们的项目网站和代码已公开。