Self-adaption multi-classifier fusion networks for image recognition

Recently, many visual recognition related studies have proved that making full use of different levels of features can effectively enhance the representational ability of convolutional neural networks (CNNs). Different from other CNN architecture which are devoted to aggregate features of different scales, we proposed a multi-classifier network (MCN) to make more effective use of these feature maps. Specifically, MCN can directly make full use of features of different levels and fuse intermediate results in a self-adaption way. Note that the auxiliary classifiers not only can optimize the internal features of CNNs directly, but also bring additional gradient which further solves the problem of vanishing-gradient. In addition, MCN is a very flexible architecture and can be combined with existing state-of-the-art networks (ResNet, DenseNet, ResNeXt, etc.) easily. Extensive experiments on three highly competitive benchmark datasets, CIFAR-10, CIFAR-100 and ImageNet, clearly demonstrate superior performance of the proposed MCN over state-of-the-arts.