Pretrained Reversible Generation as Unsupervised Visual Representation Learning

• URL: http://arxiv.org/abs/2412.01787v1
• Date: Fri, 29 Nov 2024 08:24:49 GMT
• Title: Pretrained Reversible Generation as Unsupervised Visual Representation Learning
• Authors: Rongkun Xue, Jinouwen Zhang, Yazhe Niu, Dazhong Shen, Bingqi Ma, Yu Liu, Jing Yang,
• Abstract summary: We propose Pretrained Reversible Generation (PRG), which extracts unsupervised representations by reversing the generative process of a pretrained continuous flow model. PRG effectively reuses unsupervised generative models, leveraging their high capacity to serve as robust and generalizable feature extractors for downstream tasks.
• Score: 7.076111872035312
• License:
• Abstract: Recent generative models based on score matching and flow matching have significantly advanced generation tasks, but their potential in discriminative tasks remains underexplored. Previous approaches, such as generative classifiers, have not fully leveraged the capabilities of these models for discriminative tasks due to their intricate designs. We propose Pretrained Reversible Generation (PRG), which extracts unsupervised representations by reversing the generative process of a pretrained continuous flow model. PRG effectively reuses unsupervised generative models, leveraging their high capacity to serve as robust and generalizable feature extractors for downstream tasks. Our method consistently outperforms prior approaches across multiple benchmarks, achieving state-of-the-art performance among generative model-based methods, including 78\% top-1 accuracy on ImageNet. Extensive ablation studies further validate the effectiveness of our approach.

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