论文标题
深度学习的不确定性量化和资源需求计算机视觉应用
Uncertainty Quantification and Resource-Demanding Computer Vision Applications of Deep Learning
论文作者
论文摘要
将深层神经网络（DNN）带入安全关键应用，例如自动驾驶，医学成像和金融，需要对模型的不确定性进行彻底处理。培训深层神经网络已经需要资源，因此它们的不确定性量化也是如此。在这篇概述文章中，我们调查了我们开发的方法是为了教导DNN何时遇到新对象类。此外，我们提出了培训方法，只需在不确定性量化的帮助下才从几个标签中学习。请注意，与普通网络培训相比，在计算数量级的计算时，这通常是用大量的开销来支付的。最后，我们调查了关于神经体系结构搜索的工作，这也是一项数量级，而不是普通的网络培训。
Bringing deep neural networks (DNNs) into safety critical applications such as automated driving, medical imaging and finance, requires a thorough treatment of the model's uncertainties. Training deep neural networks is already resource demanding and so is also their uncertainty quantification. In this overview article, we survey methods that we developed to teach DNNs to be uncertain when they encounter new object classes. Additionally, we present training methods to learn from only a few labels with help of uncertainty quantification. Note that this is typically paid with a massive overhead in computation of an order of magnitude and more compared to ordinary network training. Finally, we survey our work on neural architecture search which is also an order of magnitude more resource demanding then ordinary network training.
