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：尽管许多无监督学习模型专注于单一任务族（生成性或判别性），我们探索了统一表示学习器的可能性：一个能同时处理两类任务的模型。我们识别出扩散模型——生成任务中最先进的方法——作为首要候选。这类模型训练U-Net迭代预测并去除噪声，所得模型能合成高保真度、多样化、新颖的图像。我们发现U-Net的中间特征图是多样化、判别性的特征表示。我们提出了一种新型注意力机制用于池化特征图，并进一步将其作为DifFormer（一种跨不同扩散U-Net块和噪声步骤的特征的Transformer特征融合机制）利用。我们还开发了DifFeed，一种专为扩散模型定制的新型反馈机制。我们发现扩散模型优于GAN，且结合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、目标检测与分割、语义分割）上与最先进的无监督图像表示学习方法相竞争。我们的项目网站（https://mgwillia.github.io/diffssl/）和代码（https://github.com/soumik-kanad/diffssl）已公开。