在深度学习中,翻转的表现优于学
Yuxuan Liang1, Chuang Niu1, Pingkun Yan1
1Biomedical Imaging Center, Center for Biotechnology and Interdisciplinary Studies, Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA.
Visual computing for industry, biomedicine, and art
|February 22, 2024
概括
Flipover是一种新的人工神经网络技术,通过逆转神经元输出来增强模型的稳定性,在减轻过度拟合,噪音和对抗性攻击方面表现优于传统的脱落.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 人工神经网络 (ANN) 容易发生过和噪音.
- 标准的放弃技术随机关闭神经元以提高概括性.
- 需要更有效的规范化方法来增强ANN的稳定性.
研究的目的:
- 介绍Flipover,这是ANN的一种增强的学技术.
- 评估Flipover在提高模型强度方面的有效性.
- 将Flipover的性能与传统的机进行比较.
主要方法:
- 在训练过程中,Flipover随机选择神经元并用负乘数逆转它们的输出.
- 这种方法提供了较强的规范化相比,标准的脱落.
- 在各种神经网络架构上进行了实验.
主要成果:
- Flipover有效地减轻了过,实现了与dropout相当的或比dropout更好的性能.
- 该技术显著放大了对噪音数据的稳定性.
- 翻转增强了对神经网络的对抗性攻击的弹性.
结论:
- 翻转是一种高度有效的规范化技术,用于深度学习.
- 它提供了优越的强度,可以抵御过度装配,噪音和对手攻击.
- 翻转代表了提高ANN可靠性的有希望的进步.
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