SENet: Squeeze-and-Excitation Networks

URI: https://gptkb.org/entity/SENet:_Squeeze-and-Excitation_Networks
GPTKB entity
Statements (31)
Predicate Object
gptkbp:instanceOf deep learning architecture
gptkbp:appliesTo convolutional neural networks
gptkbp:category gptkb:convolutional_neural_network
computer vision
attention mechanism
gptkbp:citation over 10,000
gptkbp:contribution dynamic channel-wise feature recalibration
https://www.w3.org/2000/01/rdf-schema#label SENet: Squeeze-and-Excitation Networks
gptkbp:improves channel interdependencies modeling
gptkbp:influenced gptkb:EfficientNet
gptkb:CBAM
SKNet
gptkbp:introduced gptkb:Andrea_Vedaldi
gptkb:Gang_Sun
gptkb:Jie_Hu
gptkb:Li_Shen
Samuel Albanie
gptkbp:notablePublication gptkb:Squeeze-and-Excitation_Networks
https://arxiv.org/abs/1709.01507
gptkbp:openSource gptkb:GitHub
gptkbp:platform gptkb:TensorFlow
gptkb:PyTorch
gptkbp:proposedBy Squeeze-and-Excitation block
gptkbp:publicationYear 2018
gptkbp:publishedIn gptkb:CVPR_2018
gptkbp:usedIn image classification
object detection
semantic segmentation
gptkbp:won gptkb:ILSVRC_2017_classification_task
gptkbp:bfsParent gptkb:CVPR_2018
gptkbp:bfsLayer 7