RSNet: A Compact Relative Squeezing Net for Image Recognition Convolutional neural networks(CNN) are showing powerful performance on image recognition tasks. However, when CNN is applied to mobile devices, with limited computing and memory resource, it requires more compact design to maintain a relatively high performance. In this paper, we propose Relative Squeezing Net(RSNet) that provides technical insight into CNN structure for designing a compact model. In an endeavor to improve CondenseNet, we introduce RelativeSqueezing bottleneck where output is weighted percentage of input channels. The design of our bottleneck can transmit diverse and most useful features at all stages. We also employ multiple compression layers to constrain the output channels of feature maps which can eliminate superfluous feature maps and transmit powerful representations to next layers. We evaluate our model on two benchmark datasets; CIFAR and ImageNet. Experimental results show that RSNet achieves stateoftheart results with less parameters and FLOPs and is more efficient than compact architectures such as CondenseNet, MobileNet and ShuffleNet.

Learning Sparse Patterns in Deep Neural Networks Acceleration in deeper neural networks is hindered by high demand for computation and memory storage in resource constrained devices. In this paper, we propose a novel regularization method to learn hardwarefriendly sparse structures for deep convolutional neural networks. A limited number of fixed sparse patterns are learned through the regularization and the filters are represented by these fixed sparse patterns. Our regularization method can produce compact models with higher degree of sparsity than the stateoftheart structured sparsity learning method. The limited number of fixed sparse patterns also facilitate the hardware implementation. Our experiments on several deep neural networks with image classification tasks show that our proposed approach can achieve remarkably higher degree of sparsity with acceptable loss of accuracy.