相关实验视频
Updated: Jan 12, 2026

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Deep Neural Networks for Image-Based Dietary Assessment
Published on: March 13, 2021
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规模网:使用增量参数扩展预训练的神经网络
概括
通过使用重量共享,ScaleNet通过向预训练模型添加层次来有效地扩展视觉转换器 (ViT). 这种方法显著提高了准确性,并减少了较大的ViT模型的训练时间.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 较大的视觉变压器 (ViT) 显示出卓越的性能,但需要大量的计算资源进行培训.
- 由于培训成本和时间较高,扩展ViT模型面临重大挑战.
研究的目的:
- 引入ScaleNet,这是扩展ViT模型的高效方法.
- 通过利用预先训练的模型,实现ViT的快速和经济高效的扩展.
主要方法:
- 在预训练的ViT中,ScaleNet插入了额外的层,使用层级的权重共享来提高参数效率.
- 通过并行适配器模块引入调整参数,以优化共享重量并防止性能降低.
主要成果:
- 斯卡尔网 (ScaleNet) 促进了ViT模型的高效扩展,正如ImageNet-1K数据集所示.
- 使用ScaleNet进行的2$\times$深度缩放的DeiT-Base模型比从头开始的培训提高了7.42%的准确性.
- 与从零开始的传统培训相比,该方法只需要三分之一的培训时间.
结论:
- 斯卡尔网为扩展ViT模型提供了一个具有成本效益的解决方案,以最小的参数增加来提高性能.
- 该方法对下游视觉任务,包括对象检测,超出图像分类的承诺.
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