A-SelecT: Automatic Timestep Selection for Diffusion Transformer Representation Learning
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866912984172855296 |
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| author | Liu, Changyu Liang, James Chenhao Yang, Wenhao Cui, Yiming Yang, Jinghao Wang, Tianyang Wang, Qifan Liu, Dongfang Han, Cheng |
| author_facet | Liu, Changyu Liang, James Chenhao Yang, Wenhao Cui, Yiming Yang, Jinghao Wang, Tianyang Wang, Qifan Liu, Dongfang Han, Cheng |
| contents | Diffusion models have significantly reshaped the field of generative artificial intelligence and are now increasingly explored for their capacity in discriminative representation learning. Diffusion Transformer (DiT) has recently gained attention as a promising alternative to conventional U-Net-based diffusion models, demonstrating a promising avenue for downstream discriminative tasks via generative pre-training. However, its current training efficiency and representational capacity remain largely constrained due to the inadequate timestep searching and insufficient exploitation of DiT-specific feature representations. In light of this view, we introduce Automatically Selected Timestep (A-SelecT) that dynamically pinpoints DiT's most information-rich timestep from the selected transformer feature in a single run, eliminating the need for both computationally intensive exhaustive timestep searching and suboptimal discriminative feature selection. Extensive experiments on classification and segmentation benchmarks demonstrate that DiT, empowered by A-SelecT, surpasses all prior diffusion-based attempts efficiently and effectively. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_25758 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A-SelecT: Automatic Timestep Selection for Diffusion Transformer Representation Learning Liu, Changyu Liang, James Chenhao Yang, Wenhao Cui, Yiming Yang, Jinghao Wang, Tianyang Wang, Qifan Liu, Dongfang Han, Cheng Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Image and Video Processing Diffusion models have significantly reshaped the field of generative artificial intelligence and are now increasingly explored for their capacity in discriminative representation learning. Diffusion Transformer (DiT) has recently gained attention as a promising alternative to conventional U-Net-based diffusion models, demonstrating a promising avenue for downstream discriminative tasks via generative pre-training. However, its current training efficiency and representational capacity remain largely constrained due to the inadequate timestep searching and insufficient exploitation of DiT-specific feature representations. In light of this view, we introduce Automatically Selected Timestep (A-SelecT) that dynamically pinpoints DiT's most information-rich timestep from the selected transformer feature in a single run, eliminating the need for both computationally intensive exhaustive timestep searching and suboptimal discriminative feature selection. Extensive experiments on classification and segmentation benchmarks demonstrate that DiT, empowered by A-SelecT, surpasses all prior diffusion-based attempts efficiently and effectively. |
| title | A-SelecT: Automatic Timestep Selection for Diffusion Transformer Representation Learning |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Image and Video Processing |
| url | https://arxiv.org/abs/2603.25758 |