Abstract
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 (this https URL) and code (this https URL) are available publicly.
Abstract (translated)
虽然许多无监督学习模型集中于一个或两个家族的任务，无论是生成还是判别性的，但我们探索了统一表示学习者的可能性：一个同时处理这两个家族任务的模型。我们选中了扩散模型，这是一种最先进的生成任务方法，作为潜在的统一表示学习者的候选者。这类模型涉及训练一个U-Net以迭代预测和删除噪声，并因此得到的模型可以合成高保真度、多样、新颖的图像。我们发现U-Net的中间特征图具有多样性、判别性特征表示。我们提出了一个新的关注机制用于池化特征图，并利用这个机制作为DifFormer，一种来自不同扩散U-Net块和噪声步骤的特征变换器。我们还开发了DifFeed，一种专为扩散设计的反馈机制。我们发现，扩散模型比GAN更好，并且，通过我们的融合和反馈机制，可以与最先进的无监督图像表示学习方法竞争，这些方法可以用于全监督和半监督任务——包括带全和半监督的图像分类、微细化分类、目标检测和分割，以及语义分割。我们的项目网站（此https URL）和代码（此https URL）都是公开的。
URL
https://arxiv.org/abs/2311.17921
