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Do text-free diffusion models learn discriminative visual representations?

2023-11-29 18:59:59
Soumik Mukhopadhyay, Matthew Gwilliam, Yosuke Yamaguchi, Vatsal Agarwal, Namitha Padmanabhan, Archana Swaminathan, Tianyi Zhou, Abhinav Shrivastava

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

PDF

https://arxiv.org/pdf/2311.17921.pdf


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