Deep Residual Learning for Image Recognition
GPTKB entity
Statements (29)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
gptkb:academic_journal
|
| gptkbp:abbreviation |
ResNet paper
|
| gptkbp:arXivID |
1512.03385
|
| gptkbp:author |
gptkb:Shaoqing_Ren
gptkb:Xiangyu_Zhang gptkb:Jian_Sun gptkb:Kaiming_He |
| gptkbp:citation |
high
|
| gptkbp:contribution |
enabling very deep networks
introduction of residual connections |
| gptkbp:doi |
10.1109/CVPR.2016.90
|
| gptkbp:field |
computer vision
deep learning |
| gptkbp:impact |
state-of-the-art performance in image recognition
|
| gptkbp:influenced |
deep neural network architectures
|
| gptkbp:language |
English
|
| gptkbp:method |
residual learning framework
|
| gptkbp:pages |
770
778 |
| gptkbp:proposedBy |
gptkb:ResNet
|
| gptkbp:publicationYear |
2016
|
| gptkbp:publishedIn |
gptkb:2016_IEEE_Conference_on_Computer_Vision_and_Pattern_Recognition_(CVPR)
|
| gptkbp:roadType |
gptkb:convolutional_neural_network
|
| gptkbp:trainer |
gptkb:CIFAR-10
gptkb:CIFAR-100 gptkb:ImageNet |
| gptkbp:bfsParent |
gptkb:arXiv:1412.6980
|
| gptkbp:bfsLayer |
6
|
| https://www.w3.org/2000/01/rdf-schema#label |
Deep Residual Learning for Image Recognition
|