通过规范化学习标签噪音强大的网络:拓视图
概括
本研究介绍了网络边界拓规范化 (NBTR) 以对抗神经网络中的标签噪声. NBTR简化了类界限,提高了概括准确性和增强了防噪能力.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 计算拓学的计算拓学
背景情况:
- 神经网络与现实世界的标签噪声作斗争,这种噪声通过破坏局部合适值来降低概括性.
- 现有的规范化方法主要解决全球限制,忽视标签噪声的当地影响.
- 标签噪声对神经网络功能的特定影响需要更深入的研究.
研究的目的:
- 以拓角度分析标签噪声在神经网络上的局部影响.
- 引入一种新的规范化方法,即网络边界拓规范化 (NBTR),以减轻标签噪声.
- 提高神经网络对标签噪声的概括性能和稳定性.
主要方法:
- 开发了基于持久同质性的网络边界拓规范化 (NBTR).
- 专注于简化类边界的拓,以解决局部适合值干扰.
- 在各种数据集,网络架构和噪音类型中进行了广泛的实验.
主要成果:
- NBTR有效地减少了网络记住标签噪声的倾向.
- 该方法在概括准确性方面超过了强有力的基线,特别是在非对称的噪声条件下 (平均改善7.72%).
- 在互补使用时,NBTR增强了传统方法的抗噪声能力.
结论:
- 网络边界拓调整 (NBTR) 提供了一种新且有效的方法来解决神经网络中标签噪声的局部影响.
- 拓视角为了解和减轻标签噪声提供了有价值的见解.
- NBTR在概括和稳定性方面取得了显著的改进,使其成为现实应用的有前途的技术.
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