PaperICLR 2015 / arXiv · 2014
Very Deep Convolutional Networks for Large-Scale Image Recognition
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In the Atlas
ModelPrimary source of
VGG
Family of very deep CNN image classifiers (11 to 19 weight layers) built from stacked 3×3 convolutions with stride 1 and 2×2 max-pool stride 2, trained on ImageNet with SGD + dropout.
Cited as a reference by 5 pages, grouped by domain
Detection 1
Image formation 1
Appears in 2 narratives
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2012ImageNet Classification with Deep Convolutional Neural NetworksKrizhevsky, Sutskever, Hinton2015Fully Convolutional Networks for Semantic SegmentationLong, Shelhamer, DarrellBuilt upon by · 8
2016Deep Residual Learning for Image RecognitionHe, Zhang et al.2016You Only Look Once: Unified, Real-Time Object DetectionRedmon, Divvala et al.2018BiSeNet: Bilateral Segmentation Network for Real-time Semantic SegmentationYu, Wang et al.2019Fast-SCNN: Fast Semantic Segmentation NetworkPoudel, Liwicki, Cipolla2020A Simple Framework for Contrastive Learning of Visual RepresentationsChen, Kornblith et al.2020Bootstrap your own latent: A new approach to self-supervised LearningGrill, Strub et al.2020BiSeNet V2: Bilateral Network with Guided Aggregation for Real-time Semantic SegmentationYu, Gao et al.