Image Classification Using a Diffusion Model as a Pre-Training Model

Kosuke Ukita; Ye Xiaolong; Tsuyoshi Okita

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arXiv · 2505.06890

Image Classification Using a Diffusion Model as a Pre-Training Model

Kosuke Ukita·Ye Xiaolong·Tsuyoshi Okita

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Abstract

In this paper, we propose a diffusion model that integrates a representation-conditioning mechanism, where the representations derived from a Vision Transformer (ViT) are used to condition the internal process of a Transformer-based diffusion model. This approach enables representation-conditioned data generation, addressing the challenge of requiring large-scale labeled datasets by leveraging self-supervised learning on unlabeled data. We evaluate our method through a zero-shot classification task for hematoma detection in brain imaging. Compared to the strong contrastive learning baseline, DINOv2, our method achieves a notable improvement of +6.15% in accuracy and +13.60% in F1-score, demonstrating its effectiveness in image classification.

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BibTeX RIS

Kosuke Ukita, Ye Xiaolong, Tsuyoshi Okita. 2025-05-11. Image Classification Using a Diffusion Model as a Pre-Training Model. https://arxiv.org/abs/2505.06890

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