Transcription of Residual Attention Network for Image Classification
{{id}} {{{paragraph}}}
Example: barber
Residual Attention Network for Image Classification
Residual Attention Network for Image ClassificationFei Wang1, Mengqing Jiang2, Chen Qian1, Shuo Yang3, Cheng Li1,Honggang Zhang4, Xiaogang Wang3, Xiaoou Tang31 SenseTime Group Limited,2 Tsinghua University,3 The Chinese University of Hong Kong,4 Beijing University of Posts and Telecommunications1{wangfei, qianchen, this work, we propose Residual Attention Network ,a convolutional neural Network using Attention mechanismwhich can incorporate with state-of-art feed forward net-work architecture in an end-to-end training fashion. OurResidual Attention Network is built by stacking AttentionModules which generate Attention -aware features. Theattention-aware features from different modules changeadaptively as layers going deeper. Inside each AttentionModule, bottom-up top-down feedforward structure is usedto unfold the feedforward and feedback Attention processinto a single feedforward process. Importantly, we proposeattention Residual learning to train very deep Residual At-tention Networks which can be easily scaled up to hundredsof analyses are conducted on CIFAR-10 andCIFAR-100 datasets to verify the effectiveness of every mod-ule mentioned above.}
dation problem for deep convolutional neural network. However, recent advances of image classification focus on training feedforward convolutional neural networks us-ing “very deep” structure [27, 33, 10]. The feedforward convolutional network mimics the bottom-up paths of hu-man cortex. Various approaches have been proposed to
Tags:
Information
Domain:
Source:
Link to this page:
Please notify us if you found a problem with this document: