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English(EN) Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers

视觉Transformer利用DCT提升注意力和效率

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种利用离散余弦变换（DCT）来增强视觉Transformer的新颖方法。该方法包括一种基于DCT的自注意力初始化策略，可提高在CIFAR-10和ImageNet-1K等基准测试上的分类准确性。此外，一种基于DCT的注意力压缩技术通过截断输入块的高频分量来降低计算开销，从而在Swin Transformer等模型中保持性能。 AI

影响 引入了降低计算成本和提高视觉Transformer准确性的方法，可能促进更广泛的应用。

排序理由 学术论文，介绍了提高视觉Transformer效率和性能的新颖技术。

在 arXiv cs.CV 阅读 →

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视觉Transformer利用DCT提升注意力和效率

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学术论文，介绍了提高视觉Transformer效率和性能的新颖技术。
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Hongyi Pan, Emadeldeen Hamdan, Xin Zhu, Ahmet Enis Cetin, Ulas Bagci ·

    基于离散余弦变换的解耦注意力机制用于视觉Transformer

    arXiv:2405.13901v4 Announce Type: replace Abstract: Self-attention is central to the success of Transformer architectures; however, learning the query, key, and value projections from random initialization remains challenging and computationally expensive. In this paper, we propo…

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