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English(EN) Rethinking Depth Pruning for Vision Transformers: A Heterogeneity-Aware Perspective

新的深度剪枝方法提高了视觉Transformer的效率

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种名为HetDPT的新方法来改进视觉Transformer（ViTs）的深度剪枝。该方法考虑了不同层之间的异构性，而这是先前深度剪枝技术的局限性。HetDPT避免了维度不匹配的问题，并在ImageNet-1K和CIFAR-100等数据集上实现了显著的加速，同时保持了准确性。当与宽度剪枝结合时，HetDPT+在极端的ViT剪枝方面设定了新的最先进水平，在接近无损准确率的情况下实现了更高的加速比。 AI

影响 这项研究可能导致在各种应用中更高效、更快速地部署视觉Transformer模型。

排序理由 该集群包含一篇详细介绍优化视觉Transformer新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新的深度剪枝方法提高了视觉Transformer的效率

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该集群包含一篇详细介绍优化视觉Transformer新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Zhenfeng Su, Kang Zhao, Han Bao, Tao Yuan, Zhongzhe Hu, Xianzhi Yu, Wenxuan Wang ·

    重新思考视觉 Transformer 的深度剪枝：一种异质性感知视角

    arXiv:2607.03784v1 Announce Type: cross Abstract: While prior studies have successfully compressed vision Transformers (ViTs) through various pruning techniques, most have concentrated on width pruning to achieve significant reductions in model size. Depth pruning, which removes …

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