ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

Abstract

We introduce an extremely computation-efficient CNN architecture named ShuffleNet, which is designed specially for mobile devices with very limited computing power (e.g., 10-150 MFLOPs). The new architecture utilizes two new operations, pointwise group convolution and channel shuffle, to greatly reduce computation cost while maintaining accuracy. Experiments on ImageNet classification and MS COCO object detection demonstrate the superior performance of ShuffleNet over other structures, e.g. lower top-1 error (absolute 7.8%) than recent MobileNet [12] on Ima-geNet classification task, under the computation budget of 40 MFLOPs. On an ARM-based mobile device, ShuffleNet achieves ∼13× actual speedup over AlexNet while maintaining comparable accuracy.

Pan-cancer image-based detection of clinically actionable genetic alterations

“…Our method is a 'one-stop-shop' work-62 flow: we collected large patient cohorts for individual tumor types, partitioning each cohort into 63 tion of microsatellite instability (MSI) in colorectal cancer as a clinically relevant benchmark task 20 66 and sampled a large hyperparameter space with different commonly used deep learning mod-67 els 16,18,20,21 . Unexpectedly, 'inception' 23 and 'resnet' 24 networks, which had been the previous de-68 facto standard, were markedly outperformed by 'densenet' 25 and 'shufflenet' 14 were highly significantly detectable from histology alone, reaching AUCs of up to 0.82 in a three-79 fold patient-level cross-validation ( Fig. 1e).…”

Binary neural networks: A survey

“…They visualize the feature maps extracted by different filters and view each filter as a visual unit focusing on different visual components.of the ResNet-50 [28], and meanwhile save more than 75% of parameters and 50% computational time. In the literature, approaches for compressing the deep networks can be classified into five categories: parameter pruning [26,29,30,31], parameter quantizing [32,33,34,35,36,37,38,39,40,41], low-rank parameter factorization [42,43,44,45,46], transferred/compact convolutional filters [47,48,49,50], and knowledge distillation [51,52,53,54,55,56]. The parameter pruning and quantizing mainly focus on eliminating the redundancy in the model parameters respectively by removing the redundant/uncritical ones or compressing the parameter space (e.g.…”

Depthwise-STFT Based Separable Convolutional Neural Networks

“…Thus, the Depthwise-STFT separable layer has a lower space-time complexity when com-arXiv:2001.09912v1 [cs.CV] 27 Jan 2020 pared to the depthwise separable convolutions. Furthermore, we show experimentally that the proposed layer achieves better performance compared to the many state-of-the-art depthwise separable based models such as MobileNet [6,7] and ShuffleNet [8,9].…”