最少足够的视图:DNN模型做出更多证据的预测具有更高的准确性
Keisuke Kawano1, Takuro Kutsuna1, Keisuke Sano2
1Toyota Central R&D Labs., Inc., 41-1, Yokomichi, Nagakute, Aichi, Japan.
概括
深度神经网络 (DNN) 通过使用多个图像证据来更好地概括. 该研究引入了最小足够的视图 (MSV) 来量化这些证据,显示更多的MSV与改进的DNN概括相关.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 深度神经网络 (DNN) 在图像识别方面表现出色,但对它们的概括能力缺乏明确的解释.
- 一个领先的假设表明DNN利用多个图像衍生证据进行强有力的预测.
研究的目的:
- 调查DNN概括与从图像中提取的证据数量相关的假设.
- 引入和验证一种用于量化这些证据的新方法.
主要方法:
- 提出了最小足够视图 (MSV) 的概念,即最小的图像区域对于DNN的预测至关重要.
- 在经验上将MSV的数量与各种DNN架构的泛化性能相关联.
主要成果:
- 在不同模型中,MSV数量和DNN泛化性能之间观察到强烈的正相关性.
- 一个DNN所使用的证据的数量直接与其概括能力有关.
结论:
- DNN 使用的证据量 (MSV) 是其概括性能的一个关键因素.
- 引入了基于MSV的DNN模型选择指标,独立于标签信息,并且比基于信任的指标更可靠地超拟合.
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