Very deep convolutional neural networks (CNNs) yield state of the art results on a wide variety of visual recognition problems. A number of state of the the art methods for image recognition are based on networks with well over 100 layers and the performance vs. depth trend is moving towards networks in excess of 1000 layers. In such extremely deep architectures the vanishing or exploding gradient problem becomes a key issue. Recent evidence also indicates that convolutional networks could benefit from an interface to explicitly constructed memory mechanisms interacting with a CNN feature processing hierarchy. Correspondingly, we propose and evaluate a memory mechanism enhanced convolutional neural network architecture based on augmenting convolutional residual networks with a long short term memory mechanism. We refer to this as a convolutional residual memory network and we find that this approach can yield state of the art performance on the CIFAR-100 and SVHN benchmarks. This is achieved using a network with more breadth, much less depth and much less overall computation relative to comparable models without the memory mechanism. Our experiments and analysis explore the importance of the memory mechanism, network depth, breadth, and predictive performance.

通过在卷积残差网络中引入长短时记忆机制，增强了记忆机制加强卷积神经网络的性能，使得该方法在CIFAR-100基准测试上优于其他最先进的技术，并且在CIFAR-10和SVHN基准测试上与之相当，而且相比于深度相当的深度残差神经网络，网络宽度更大，深度更小，总体计算量更少。

卷积残差记忆网络