Squeeze-and-Excitation Networks

URI: https://gptkb.org/entity/Squeeze-and-Excitation_Networks
GPTKB entity
Statements (32)
Predicate Object
gptkbp:instanceOf gptkb:convolutional_neural_network
gptkbp:abbreviation gptkb:SENet
gptkbp:appliesTo convolutional neural networks
gptkbp:arXivID 1709.01507
gptkbp:category computer vision
deep learning
gptkbp:citation over 10,000
gptkbp:component excitation operation
scaling operation
squeeze operation
gptkbp:contribution channel-wise feature recalibration
https://www.w3.org/2000/01/rdf-schema#label Squeeze-and-Excitation Networks
gptkbp:implementedIn gptkb:TensorFlow
gptkb:PyTorch
gptkbp:improves image classification accuracy
gptkbp:influenced gptkb:EfficientNet
gptkb:CBAM
gptkbp:introduced gptkb:Gang_Sun
gptkb:Jie_Hu
gptkb:Li_Shen
gptkbp:introducedIn 2017
gptkbp:language gptkb:Python
gptkbp:license open source
gptkbp:notablePublication gptkb:Squeeze-and-Excitation_Networks
gptkbp:pdf https://arxiv.org/abs/1709.01507
gptkbp:publishedIn gptkb:CVPR_2018
gptkbp:usedIn gptkb:Inception
gptkb:ResNet
gptkb:MobileNet
gptkbp:won gptkb:ILSVRC_2017_classification_task
gptkbp:bfsParent gptkb:CVPR_2017
gptkbp:bfsLayer 6