论文标题
关于MC辍学行为的注释
Notes on the Behavior of MC Dropout
论文作者
论文摘要
在估计深层神经网络中不确定性的各种选择中，蒙特卡洛辍学者以其简单性和有效性而广受欢迎。但是，必须仔细考虑并测试通过这种方法估算的不确定性质量以及在培训程序中的选择各不相同，以获得令人满意的结果。在本文中，我们介绍了一项研究，该研究对蒙特 - 卡洛辍学的行为有了不同的观点，这使我们能够观察到该技术的一些有趣的属性，以在考虑其用于不确定性估计的使用时牢记。
Among the various options to estimate uncertainty in deep neural networks, Monte-Carlo dropout is widely popular for its simplicity and effectiveness. However the quality of the uncertainty estimated through this method varies and choices in architecture design and in training procedures have to be carefully considered and tested to obtain satisfactory results. In this paper we present a study offering a different point of view on the behavior of Monte-Carlo dropout, which enables us to observe a few interesting properties of the technique to keep in mind when considering its use for uncertainty estimation.
