Although deeper and larger neural networks have achieved better performance,
the complex network structure and increasing computational cost cannot meet the
demands of many resource-constrained applications. Existing methods usually
choose to execute or skip an entire specific layer, which can only alter the
depth of the network...
In this paper, we propose a novel method called Dynamic
Multi-path Neural Network (DMNN), which provides more path selection choices in
terms of network width and depth during inference. The inference path of the
network is determined by a controller, which takes into account both previous
state and object category information. The proposed method can be easily
incorporated into most modern network architectures. Experimental results on
ImageNet and CIFAR-100 demonstrate the superiority of our method on both
efficiency and overall classification accuracy. To be specific, DMNN-101
significantly outperforms ResNet-101 with an encouraging 45.1% FLOPs reduction,
and DMNN-50 performs comparably to ResNet-101 while saving 42.1% parameters.