Despite the popularity and success of deep learning, there is limited understanding of when, how, and why neural networks generalize to unseen examples. Since learning can be seen as extracting information from data, we formally study information captured by neural networks during training. Specifically, we start with viewing learning in presence of noisy labels from an information-theoretic perspective and derive a learning algorithm that limits label noise information in weights. We then define a notion of unique information that an individual sample provides to the training of a deep network, shedding some light on the behavior of neural networks on examples that are atypical, ambiguous, or belong to underrepresented subpopulations. We relate example informativeness to generalization by deriving nonvacuous generalization gap bounds. Finally, by studying knowledge distillation, we highlight the important role of data and label complexity in generalization. Overall, our findings contribute to a deeper understanding of the mechanisms underlying neural network generalization.

通过信息论分析深度学习及神经网络，研究在训练过程中神经网络获取的信息，提出了限制权值中标签噪声信息的学习算法，并提出了样本信息度量概念，探究了神经网络在非典型、模棱两可或属于代表性较弱群体的情况下的行为，导出了非平凡的泛化表现界限，并通过知识蒸馏研究，证明了数据和标签复杂度在泛化中的重要作用。该论文深入研究了神经网络的泛化机制，有益于更深层次地理解此领域的相关知识。

论神经网络所学习的信息：与记忆和泛化的联系