Paper Info
Title
Espnetv2: A Light-Weight, Power Efficient, And General Purpose Convolutional Neural Network
Abstract
We introduce a light-weight, power efficient, and general purpose convolutional neural network, ESPNetv2, for modeling visual and sequential data. Our network uses group point-wise and depth-wise dilated separable convolutions to learn representations from a large effective receptive field with fewer FLOPs and parameters. The performance of our network is evaluated on four different tasks: (I) object classification, (2) semantic segmentation, (3) object detection, and (4) language modeling. Experiments on these tasks, including image classification on the ImageNet and language modeling on the PenTree bank dataset, demonstrate the superior performance of our method over the state-of-the-art methods. Our network outperforms ESPNet by 4-5% and has 2-4x fewer FLOPs on the PASCAL VOC and the Cityscapes dataset. Compared to YOLOv2 on the MS-COCO object detection, ESPNetv2 delivers 4.4% higher accuracy with 6x fewer FLOPs. Our experiments show that ESPNetv2 is much more power efficient than existing state-of-the-art efficient methods including ShuffleNets and MobileNets. Our code is open-source and available at https://qithub.com/sachmehta/ESPNetv2.
Year
DOI
Venue
2018
10.1109/CVPR.2019.00941
2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019)
Field
DocType
Volume
Object detection,Pattern recognition,Convolution,FLOPS,Segmentation,Convolutional neural network,Computer science,Separable space,Artificial intelligence,Contextual image classification,Machine learning,Language model
Journal
abs/1811.11431
ISSN
Citations 
PageRank 
1063-6919
6
0.43
References 
Authors
31
4
Name
Order
Citations
PageRank
Sachin Mehta1145.06
Mohammad Rastegari271733.88
Linda G. Shapiro32603847.56
Hannaneh Hajishirzi441746.10