-
http://arxiv.org/abs/1207.0580
-
http://arxiv.org/abs/1207.0580.[10]GaoHuang,ZhuangLiu,KilianQWeinberger,andLaurensvanderMaaten.Denselyconnectedconvolutionalnetworks.InConferenceonComputerVisionandPatternRecognition
-
http://arxiv.org/abs/1502.04585
-
http://arxiv.org/abs/1502.04585.2AdamCoates,AndrewNg,andHonglakLee.Ananalysisofsingle-layernetworksinunsupervisedfeaturelearning.InConferenceonArti
-
http://arxiv.org/abs/1512.03385.8KaimingHe,XiangyuZhang,ShaoqingRen,andJianSun.Identitymappingsindeepresidualnetworks.InEuropeanConferenceonComputerVision
-
http://arxiv.org/abs/1603.05027.9Geo
-
http://arxiv.org/abs/1608.06993.[11]KenjiKawaguchi,LesliePackKaelbling,andYoshuaBengio.Generalizationindeeplearning.2017.https://arxiv.org/abs/1710.05468.[12]AlexKrizhevsky.Learningmultiplelayersoffeaturesfromtinyimages.2009.https://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf.[13]AlexKrizhevsky,IlyaSutskever,andGeo
-
http://arxiv.org/abs/1610.02915.7KaimingHe,XiangyuZhang,ShaoqingRen,andJianSun.Deepresiduallearningforimagerecognition.InConferenceonComputerVisionandPatternRecognition
-
http://arxiv.org/abs/1611.05431.[21]YoshihiroYamada,MasakazuIwamura,andKoichiKise.Shakedropregularization.2018.https://arxiv.org/abs/1802.02375.[22]SergeyZagoruykoandNikosKomodakis.Wideresidualnetworks.2016.https://arxiv.org/abs/1605.07146.[23]BarretZoph,VijayVasudevan,JonathonShlens,andQuocVLe.Learningtransferablearchitecturesforscalableimagerecognition.2017.https://arxiv.org/abs/1707.07012.15
-
http://arxiv.org/abs/1802.01548
-
http://arxiv.org/abs/1802.01548.[18]KarenSimonyanandAndrewZisserman.Verydeepconvolutionalnetworksforlarge-scaleimagerecognition.2014.https://arxiv.org/abs/1409.1556.[19]AntonioTorralba,RobFergus,andWilliam.T.Freeman.80MillionTinyImages:ALargeDataSetforNonparametricObjectandSceneRecognition.IEEETransactionsonPatternAnalysisandMachineIntelligence,2008.https://ieeexplore.ieee.org/document/4531741/.[20]SainingXie,RossGirshick,PiotrDoll
-
http://doi.acm.org/10.1145/219717.219748
-
http://doi.acm.org/10.1145/219717.219748.[16]AliRahimiandBenjaminRecht.WeightedSumsofRandomKitchenSinks:Replacingminimizationwithrandomizationinlearning.InAdvancesinNeuralInformationProcessingSystems
-
http://ieeexplore.ieee.org/document/7486599/.14
-
http://lis.csail.mit.edu/code/gdl.html
-
http://lis.csail.mit.edu/code/gdl.htmldensenet_BC_100_12[10]hysts/pytorch_image_classification/pyramidnet_basic_110_2706hysts/pytorch_image_classification/pyramidnet_basic_110_846hysts/pytorch_image_classification/resnet_basic_1107hysts/pytorch_image_classification/resnet_basic_327hysts/pytorch_image_classification/resnet_basic_447hysts/pytorch_image_classification/resnet_basic_567hysts/pytorch_image_classification/resnet_preact_tf7tensorflow/models/../slim/nets/resnet_v2.pyresnet_v2_basic_1108hysts/pytorch_image_classification/resnet_v2_bottleneck_1648hysts/pytorch_image_classification/resnext_29_4x64d[20]hysts/pytorch_image_classification/resnext_29_8x64d[20]hysts/pytorch_image_classification/shake_drop[21]imenurok/ShakeDropshake_shake_32d4hysts/pytorch_image_classification/shake_shake_64d_cutout[3,4]hysts/pytorch_image_classification/shake_shake_64d4hysts/pytorch_image_classification/shake_shake_96d4hysts/pytorch_image_classification/vgg16_keras[14,18]geifmany/cifar-vggvgg_15_BN_64[14,18]hysts/pytorch_image_classification/wide_resnet_28_10_cutout[3,22]hysts/pytorch_image_classification/wide_resnet_28_10[22]hysts/pytorch_image_classification/wide_resnet_tf[22]tensorflow/models/tree/master/resnetTable3:Coderepositoriesfordeepmodels.Withtheexceptionofdarc,repositoriesarehostedathttps://github.com/Table3containsthecoderepositoriesforthedeepmodel.Thespeci
-
http://papers.nips.cc/paper/3495-weighted-sums-of-random-kitchen-sinks-replacing-minimization-with-randomization-in-learning.[17]EstebanReal,AlokAggarwal,YanpingHuang,andQuocV.Le.Regularizedevolutionforimageclassi
-
http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.[14]ShuyingLiuandWeihongDeng.Verydeepconvolutionalneuralnetworkbasedimageclassi
-
http://proceedings.mlr.press/v15/coates11a.html
-
http://proceedings.mlr.press/v15/coates11a.html.3TerranceDeVriesandGrahamWTaylor.Improvedregularizationofconvolutionalneuralnetworkswithcutout.2017.https://arxiv.org/abs/1708.04552.4XavierGastaldi.Shake-shakeregularization.2017.https://arxiv.org/abs/1705.07485.5BenHamner.Populardatasetsovertime.https://www.kaggle.com/benhamner/popular-datasets-over-time/code.6DongyoonHan,JiwhanKim,andJunmoKim.Deeppyramidalresidualnetworks.InConferenceonComputerVisionandPatternRecognition
-
https://arxiv.org/abs/1409.1556
-
https://arxiv.org/abs/1512.03385
-
https://arxiv.org/abs/1603.05027
-
https://arxiv.org/abs/1605.07146
-
https://arxiv.org/abs/1608.06993
-
https://arxiv.org/abs/1610.02915
-
https://arxiv.org/abs/1611.05431
-
https://arxiv.org/abs/1705.07485
-
https://arxiv.org/abs/1707.07012
-
https://arxiv.org/abs/1708.04552
-
https://arxiv.org/abs/1710.05468
-
https://arxiv.org/abs/1802.02375
-
https://github.com/
-
https://ieeexplore.ieee.org/document/4531741/
-
https://ieeexplore.ieee.org/document/7486599/
-
https://papers.nips.cc/paper/3495-weighted-sums-of-random-kitchen-sinks-replacing-minimization-with-randomization-in-learning
-
https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks
-
https://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf
-
https://www.kaggle.com/benhamner/popular-datasets-over-time/code
-
Show all
