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In visual recognition, the key to the performance improvement of ResNet isthe success in establishing the stack of deep sequential convolutional layersusing identical mapping by a shortcut connection. It results in multiple pathsof data flow under a network and the paths are merged with the equal weights.However, it is questionable whether it is correct to use the fixed andpredefined weights at the mapping units of all paths. In this paper, weintroduce the active weighted mapping method which infers proper weight valuesbased on the characteristic of input data on the fly. The weight values of eachmapping unit are not fixed but changed as the input image is changed, and themost proper weight values for each mapping unit are derived according to theinput image. For this purpose, channel-wise information is embedded from boththe shortcut connection and convolutional block, and then the fully connectedlayers are used to estimate the weight values for the mapping units. We trainthe backbone network and the proposed module alternately for a more stablelearning of the proposed method. Results of the extensive experiments show thatthe proposed method works successfully on the various backbone architecturesfrom ResNet to DenseNet. We also verify the superiority and generality of theproposed method on various datasets in comparison with the baseline.

Residual Convolutional Neural Network Revisited with Active Weighted Mapping