ReLU,稀疏性,以及神经网络中光流的编码
Oliver W Layton1, Siyuan Peng1,2, Scott T Steinmetz3
1Department of Computer Science, Colby College, Waterville, ME 04901, USA.
Sensors (Basel, Switzerland)
|December 17, 2024
概括
修正线性单元 (ReLU) 和漏洞的 ReLU 激活功能提高了神经网络中自动运动估计的准确性和稳定性. 这些功能促进了较少的数据表示,提高了机器人导航的性能.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 准确的自动运动估计对于移动机器人导航至关重要,特别是在GPS被拒绝的地区.
- 来自相机传感器的光流是估计自动运动的关键方法.
研究的目的:
- 调查不同激活函数 (ReLU,泄漏的ReLU,GELU,Mish) 如何影响神经网络性能,以从光流中估计自动运动.
- 分析对准确性,稳定性和内部数据编码属性的影响.
主要方法:
- 训练有素的卷积神经网络 (CNN) 和使用光流数据的多层感知器 (MLP).
- 在不同的条件下,通过各种激活功能评估网络性能.
主要成果:
- 使用ReLU和泄漏ReLU的网络在从新型光流模式中估计自动运动时表现出卓越的准确性.
- 这些网络在具有挑战性的环境条件下也表现出更强的稳定性.
- 与GELU和Mish相比,ReLU和泄漏的ReLU引发了较少的数据表示.
结论:
- ReLU和泄漏的 ReLU激活功能是提高神经网络中自动运动估计准确性和稳定性的最佳选择.
- 这些功能的诱导稀疏性在提高基于光流的机器人导航性能方面发挥着重要作用.
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