  Understanding generalization in deep learning is arguably one of the most
important questions in deep learning. Deep learning has been successfully
adopted to a large number of problems ranging from pattern recognition to
complex decision making, but many recent researchers have raised many concerns
about deep learning, among which the most important is generalization. Despite
numerous attempts, conventional statistical learning approaches have yet been
able to provide a satisfactory explanation on why deep learning works. A recent
line of works aims to address the problem by trying to predict the
generalization performance through complexity measures. In this competition, we
invite the community to propose complexity measures that can accurately predict
generalization of models. A robust and general complexity measure would
potentially lead to a better understanding of deep learning's underlying
mechanism and behavior of deep models on unseen data, or shed light on better
generalization bounds. All these outcomes will be important for making deep
learning more robust and reliable.
