渐进式深度增长通过更少的计算提升视觉Transformer的性能

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-15 04:00

研究人员探索了自监督视觉Transformer中渐进式深度增长的益处，特别是在MAE和DINOv3框架内。他们的发现表明，这种训练策略在全局和密集任务上均能提高下游性能，同时将计算成本降低高达24.8%。研究还揭示，渐进式深度增长能增强中间层的性能，并改变内部计算模式（如注意力机制），从而实现更高效、更有效的视觉表示学习。 AI

影响这项研究为视觉Transformer提供了一种更有效的预训练策略，有望降低视觉任务中AI模型的计算成本并提高性能。

排序理由详细介绍视觉Transformer新训练方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Amir Mohammad Karimi Mamaghan, Ferdinand Kapl, Andrea Dittadi, Karl Henrik Johansson, Kaitlin Maile, Johannes von Oswald, Stefan Bauer · 2026-10-15 04:00

Growing to See: Efficient Pretraining of Self-Supervised Vision Transformers

arXiv:2610.16817v1 Announce Type: cross Abstract: Progressive depth growth builds large networks by starting with shallow models and inserting layers during training. Recent advances in large language models have renewed interest in this longstanding approach, demonstrating benef…

本文如何被排名

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Growing to See: Efficient Pretraining of Self-Supervised Vision Transformers

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