ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
Xiangyu Zhang,Xinyu Zhou,Mengxiao Lin,Jian Sun +3 more
- pp 6848-6856
Reads0
Chats0
TLDR
ShuffleNet as discussed by the authors utilizes two new operations, pointwise group convolution and channel shuffle, to greatly reduce computation cost while maintaining accuracy, and achieves an actual speedup over AlexNet while maintaining comparable accuracy.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 ImageNet classification task, under the computation budget of 40 MFLOPs. On an ARM-based mobile device, ShuffleNet achieves ~13A— actual speedup over AlexNet while maintaining comparable accuracy.read more
Citations
More filters
Journal ArticleDOI
Toward point-of-care ultrasound estimation of fetal gestational age from the trans-cerebellar diameter using CNN-based ultrasound image analysis.
Mohammed A. Maraci,Mohammad Yaqub,Rachel Craik,Rachel Craik,Sridevi Beriwal,A. Self,Peter von Dadelszen,Aris T. Papageorghiou,J. Alison Noble +8 more
TL;DR: A machine-learning-based algorithm pipeline designed to automatically estimate the gestational age of a fetus from a short fetal ultrasound scan is proposed, designed to be realizable as a point-of-care ultrasound (POCUS) solution with an affordable wireless ultrasound probe, a smartphone or tablet, and automated machine-learned image processing.
Journal ArticleDOI
Multimodal Fusion Convolutional Neural Network With Cross-Attention Mechanism for Internal Defect Detection of Magnetic Tile
TL;DR: In this paper , a multimodal fusion convolutional neural network (MMFCNN) was proposed for internal defect detection of magnetic tile in an end-to-end way.
Journal ArticleDOI
A hybrid strategy on combining different optimization algorithms for hazardous gas source term estimation in field cases
TL;DR: A hybrid strategy was proposed in this study to combine optimization algorithms with different characteristics, including particle swarm optimization, genetic algorithm and simulated annealing algorithm, to not only achieve high accuracy in global searching, but also converge to a stable result efficiently.
Journal ArticleDOI
Deep Learning for COVID-19 Diagnosis from CT Images
TL;DR: VGG19 architecture showed promising performance in the classification of COVID-19 cases and the cross-dataset experiments exposed the critical weakness of classifying images from heterogeneous data sources, compatible with a real-world scenario.
Proceedings ArticleDOI
Complementary Relation Contrastive Distillation
Jinguo Zhu,Shixiang Tang,Dapeng Chen,Shijie Yu,Yakun Liu,Mingzhe Rong,Aijun Yang,Xiaohua Wang +7 more
TL;DR: Zhang et al. as discussed by the authors proposed complementary relation contrastive distillation (CRCD) to transfer the structural knowledge from the teacher to the student by estimating the mutual relation in an anchor-based way and distill the anchor-student relation under the supervision of its corresponding anchor-teacher relation.
References
More filters
Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
Proceedings ArticleDOI
ImageNet: A large-scale hierarchical image database
TL;DR: A new database called “ImageNet” is introduced, a large-scale ontology of images built upon the backbone of the WordNet structure, much larger in scale and diversity and much more accurate than the current image datasets.