Learning deeper convolutional neural networks becomes a tendency in recent
years. However, many empirical evidences suggest that performance improvement
cannot be gained by simply stacking more layers. In this paper, we consider the
issue from an information theoretical perspective, and propose a novel method
Relay Backpropagation, that encourages the propagation of effective information
through the network in training stage. By virtue of the method, we achieved the
first place in ILSVRC 2015 Scene Classification Challenge. Extensive
experiments on two challenging large scale datasets demonstrate the
effectiveness of our method is not restricted to a specific dataset or network
architecture. Our models will be available to the research community later.

本文从信息理论的角度考虑深度卷积神经网络的学习，提出了一种新的方法 Relay Backpropagation，在训练阶段鼓励网络中有效信息的传播，取得了 ILSVRC 2015 场景分类挑战赛的第一名。在两个具有挑战性的大规模数据集上的广泛实验表明，我们的方法的有效性不限于特定的数据集或网络架构。