Name Memory Enhanced Convolution aka Convolutional Residual Memory Networks

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References

http://arxiv.org/pdf/1606.05262v3.pdf Convolutional Residual Memory Networks

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.

This is achieved using networks with more breadth, much less depth and much less overall computation relative to comparable deep ResNets without the memory mechanism. Our experiments and analysis explore the importance of the memory mechanism, network depth, breadth, and predictive performance.