Do text-free diffusion models learn discriminative visual representations?

• URL: http://arxiv.org/abs/2311.17921v2
• Date: Thu, 30 Nov 2023 03:02:58 GMT
• Title: Do text-free diffusion models learn discriminative visual representations?
• Authors: Soumik Mukhopadhyay and Matthew Gwilliam and Yosuke Yamaguchi and Vatsal Agarwal and Namitha Padmanabhan and Archana Swaminathan and Tianyi Zhou and Abhinav Shrivastava
• Abstract summary: We explore the possibility of a unified representation learner: a model which addresses both families of tasks simultaneously. We develop diffusion models, a state-of-the-art method for generative tasks, as a prime candidate. 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.
• Score: 43.05419164830729
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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 (https://mgwillia.github.io/diffssl/) and code (https://github.com/soumik-kanad/diffssl) are available publicly.

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