foobarNet: ディープラーニング関連の○○Netまとめ

## はじめに

<blockquote class="twitter-tweet" data-lang="ja"><p lang="ja" dir="ltr">もう既に日本の機械学習界隈からも同様の会話は聞こえてくるんだけど、今現在のDeep Learningの研究開発は「こんな凄いネットワーク思い付きました選手権」と「考案したネットワークを巨大なデータセットとGPUン千枚並べて実装と実証してみました選手権」状態になりつつある気がする</p>&mdash; TJO (@TJO_datasci) <a href="https://twitter.com/TJO_datasci/status/810000895211487232">2016年12月17日</a></blockquote>

この記事では、そんな「こんな凄いネットワーク思い付きました選手権」の代表選手として、
○○Netと名の付くネットワーク（＋α）をまとめてみました。
本当にそれだけの内容なのでまともなまとめは以下をご覧下さい。

[DeepLearning研究 2016年のまとめ](http://qiita.com/eve_yk/items/f4b274da7042cba1ba76)
[2016年のディープラーニング論文100選](http://qiita.com/sakaiakira/items/9da1edda802c4884865c)
[The major advancements in Deep Learning in 2016](https://tryolabs.com/blog/2016/12/06/major-advancements-deep-learning-2016/)


##Network (1 character)
[C-Net (SketchNet)](http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Zhang_SketchNet_Sketch_Classification_CVPR_2016_paper.pdf)
[ENet](https://arxiv.org/abs/1606.02147)
[HNet](https://arxiv.org/abs/1612.04642)
[KNET](https://arxiv.org/abs/1407.1687)
[MNet](https://www.cityscapes-dataset.com/method-details/?submissionID=352)
[PNET](http://www.cvlibs.net/datasets/kitti/eval_object_detail.php?result=6c781edba670537bb325fd6d02fe68b524139fd6)
[R-Net (SketchNet)](http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Zhang_SketchNet_Sketch_Classification_CVPR_2016_paper.pdf)
[S-Net (SketchNet)](http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Zhang_SketchNet_Sketch_Classification_CVPR_2016_paper.pdf)
[S-Net (source network)](https://arxiv.org/abs/1604.01545)
[T-Net (target network)](https://arxiv.org/abs/1604.01545)
[Tnet (Tyrolean network)](https://arxiv.org/abs/1511.06437)
[U-Net](https://arxiv.org/abs/1505.04597)
[V-Net](https://arxiv.org/abs/1606.04797)


##Network
[3D ShapeNet](https://arxiv.org/abs/1406.5670)
[3D U-Net](https://arxiv.org/abs/1606.06650)
[ActionFlowNet](https://arxiv.org/abs/1612.03052)
[AdaNet](https://arxiv.org/abs/1607.01097)
[ADMM-Net](http://papers.nips.cc/paper/6406-deep-admm-net-for-compressive-sensing-mri.pdf)
[AlexNet](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf)
[ANP-Net](https://arxiv.org/abs/1511.06838)
[ATGV-Net](https://arxiv.org/abs/1607.07988)
[AtomNet](https://arxiv.org/abs/1510.02855)
[AttentionNet](https://arxiv.org/abs/1506.07704)
[AttractioNet](https://arxiv.org/abs/1606.04446)
[Auto-Zoom Net (AZN)](https://arxiv.org/abs/1511.06881)
[AZ-Net](https://arxiv.org/abs/1512.07711)
[BASS Net](https://arxiv.org/abs/1612.00144)
[BGNet (without Objects)](https://arxiv.org/abs/1611.06596)
[BinaryNet (Binarized Neural Networks)](https://arxiv.org/abs/1602.02830)
[BM-NET](http://www.cvlibs.net/datasets/kitti/eval_object_detail.php?result=7c16dad7bea4194c593973909cace1380cf9b0e8)
[BranchyNet](http://www.eecs.harvard.edu/~htk/publication/2016-icpr-teerapittayanon-mcdanel-kung.pdf)
[BubbLeNet](http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Matzen_BubbLeNet_Foveated_Imaging_ICCV_2015_paper.pdf)
[ByteNet](https://arxiv.org/abs/1610.10099)
[ContextLocNet](https://arxiv.org/abs/1609.04331)
[CrowdNet](https://arxiv.org/abs/1608.06197)
[CSNet](https://arxiv.org/abs/1409.7307)
[CSVideoNet](https://arxiv.org/abs/1612.05203)
[CUNet](https://arxiv.org/abs/1607.01577)
[DAP3D-Net](https://arxiv.org/abs/1602.03346)
[dasNet](https://arxiv.org/abs/1407.3068)
[DCTNet](https://arxiv.org/abs/1507.02049)
[DeconvNet](https://arxiv.org/abs/1505.04366)
[DecoupledNet](https://arxiv.org/abs/1506.04924)
[Deep Fried Convnet](https://arxiv.org/abs/1412.7149)
[DeepID-Net](https://arxiv.org/abs/1412.5661)
[Deeply-Fused Net (DFN)](https://arxiv.org/abs/1605.07716)
[Deeply-Supervised Net (DSN)](https://arxiv.org/abs/1409.5185)
[DeepSetNet](https://arxiv.org/abs/1611.08998)
[DeepSqueeNet](http://host.robots.ox.ac.uk:8080/leaderboard/displaylb.php?challengeid=11&compid=6#KEY_DeepSqueeNet)
[DehazeNet](https://arxiv.org/abs/1601.07661)
[DeLight-Net](https://arxiv.org/abs/1603.08240)
[DelugeNet](https://arxiv.org/abs/1611.05552)
[DeMeshNet](https://arxiv.org/abs/1611.05271)
[DenseNet (Densely Connected)](https://arxiv.org/abs/1608.06993)
[DenseNet (Pyramid)](https://arxiv.org/abs/1404.1869)
[DetectNet](https://github.com/NVIDIA/DIGITS/tree/master/examples/object-detection)
[DetectorNet](http://papers.nips.cc/paper/5207-deep-neural-networks-for-object-detection.pdf)
[DevNet](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Gan_DevNet_A_Deep_2015_CVPR_paper.pdf)
[DHSNet](http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Liu_DHSNet_Deep_Hierarchical_CVPR_2016_paper.pdf)
[DispNet](https://arxiv.org/abs/1512.02134)
[DLANet](http://www.hcii-lab.net/download/papers/journal/DLANet%20A%20Manifold-Learning-based%20Discriminative%20Feature%20Learning%20Network%20for%20Scene%20Classification.pdf)
[DoReFa-Net](https://arxiv.org/abs/1606.06160)
[DPPnet](https://arxiv.org/abs/1511.05756)
[DualNet](https://arxiv.org/abs/1606.06108)
[EEGNet](https://arxiv.org/abs/1611.08024)
[EndoNet](https://arxiv.org/abs/1602.03012)
[EventNet](https://arxiv.org/abs/1506.02328)
[FaceNet](https://arxiv.org/abs/1503.03832)
[FaceNet2ExpNet](https://arxiv.org/abs/1609.06591)
[Fact-Net (ANP-Net)](https://arxiv.org/abs/1511.06838)
[FashionNet](http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Liu_DeepFashion_Powering_Robust_CVPR_2016_paper.pdf)
[FC-DenseNet](https://arxiv.org/abs/1611.09326)
[FeatMap-Net](https://arxiv.org/abs/1504.01013)
[FGNet (without Objects)](https://arxiv.org/abs/1611.06596)
[FisherNet](https://arxiv.org/abs/1608.00182)
[FitNet](https://arxiv.org/abs/1412.6550)
[FitResNet](https://arxiv.org/abs/1511.06422)
[FlowNet](https://arxiv.org/abs/1504.06852)
[FlowNet (2.0)](https://arxiv.org/abs/1612.01925)
[Fork-Net (ANP-Net)](https://arxiv.org/abs/1511.06838)
[FoveaNet (BubbLeNet)](http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Matzen_BubbLeNet_Foveated_Imaging_ICCV_2015_paper.pdf)
[FoveaNet (Xin Li+)](https://www.cityscapes-dataset.com/method-details/?submissionID=282)
[FractalNet](https://arxiv.org/abs/1605.07648)
[FusionNet (connectomics)](https://arxiv.org/abs/1612.05360)
[FusionNet (ECT)](https://arxiv.org/abs/1611.10152)
[FusionNet (V-CNN)](https://arxiv.org/abs/1607.05695)
[GBD-Net](https://arxiv.org/abs/1610.02579)
[GENet](https://arxiv.org/abs/1409.7313)
[GeThR-Net](https://arxiv.org/abs/1609.05281)
[GoogLeNet](https://arxiv.org/abs/1409.4842)
[Hierarchical Auto-Zoom Net (HAZN)](https://arxiv.org/abs/1511.06881)
[HybridNet (without Objects)](https://arxiv.org/abs/1611.06596)
[HyperNet](https://arxiv.org/abs/1604.00600)
[IDNet](https://arxiv.org/abs/1606.03238)
[Implicit ReasoNet](https://arxiv.org/abs/1611.04642)
[Inception-ResNet](https://arxiv.org/abs/1602.07261)
[Inside-Outside Net (ION)](https://arxiv.org/abs/1512.04143)
[I-PyraNet](https://pdfs.semanticscholar.org/3d67/ece2d25d49a92e558e8897d620ba90056c32.pdf)
[LDANet](https://arxiv.org/abs/1404.3606)
[LeNet](http://vision.stanford.edu/cs598_spring07/papers/Lecun98.pdf)
[LIPNet (Lateral Inhibition)](https://pdfs.semanticscholar.org/8fa2/28adbe164fb3f4566930c668be501b208388.pdf)
[LipNet (Lipreading)](https://arxiv.org/abs/1611.01599)
[LLNet](https://arxiv.org/abs/1511.03995)
[LocNet](http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Gidaris_LocNet_Improving_Localization_CVPR_2016_paper.pdf)
[LOGO-Net](https://arxiv.org/abs/1511.02462)
[M2S-Net](https://arxiv.org/abs/1604.05519)
[MatchNet](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Han_MatchNet_Unifying_Feature_2015_CVPR_paper.pdf)
[MDNet](https://arxiv.org/abs/1510.07945)
[MemNet](http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Khosla_Understanding_and_Predicting_ICCV_2015_paper.pdf)
[MLDANet](https://arxiv.org/abs/1411.1172)
Mobilenet (CVPR 2017 submission)
[MultiPathNet](https://arxiv.org/abs/1604.02135)
[Multi-ResNet](https://arxiv.org/abs/1609.05672)
[Net2Net](https://arxiv.org/abs/1511.05641)
[NVDriveNet-H](http://www.cvlibs.net/datasets/kitti/eval_object_detail.php?result=1d265d8cacc4afcea91d5c52297930d1359c42b7)
[NVSegNet](https://www.cityscapes-dataset.com/method-details/?submissionID=122)
[OctNet](https://arxiv.org/abs/1611.05009)
[OnionNet](https://arxiv.org/abs/1608.02728)
[OrigNet (without Objects)](https://arxiv.org/abs/1611.06596)
[Pairwise-Net (FeatMap-Net)](https://arxiv.org/abs/1504.01013)
[ParseNet](https://arxiv.org/abs/1506.04579)
[PCANet](https://arxiv.org/abs/1404.3606)
[PersonNet](https://arxiv.org/abs/1601.07255)
[PhaseNet (EndoNet)](https://arxiv.org/abs/1602.03012)
[PHOCNet](https://arxiv.org/abs/1604.00187)
[PixelNet](https://arxiv.org/abs/1609.06694)
[PlaNet](https://arxiv.org/abs/1602.05314)
[PN-Net](https://arxiv.org/abs/1601.05030)
[PointNet (A. Garcia-Garcia+)](http://ieeexplore.ieee.org/document/7727386/)
[PointNet (Charles R. Qi+)](https://arxiv.org/abs/1612.00593)
[PolyNet](https://arxiv.org/abs/1611.05725)
[PoseNet](https://arxiv.org/abs/1505.07427)
[PredNet](https://arxiv.org/abs/1605.08104)
[PrimaryNet (ECT)](https://arxiv.org/abs/1611.10152)
[ProductNet](http://www.cs.cornell.edu/~kb/publications/SIG15ProductNet.pdf)
[Progressive Net](https://arxiv.org/abs/1610.04286)
[ProNet](https://arxiv.org/abs/1511.03776)
[PSPNet](https://arxiv.org/abs/1612.01105)
[PupilNet](https://arxiv.org/abs/1601.04902)
[PVANET](https://arxiv.org/abs/1611.08588)
[PyramidNet](https://arxiv.org/abs/1610.02915)
[PyraNet](http://www.uow.edu.au/~phung/docs/2007-TNN-pyramidal-neural-network.pdf)
[RandNet](https://arxiv.org/abs/1404.3606)
[RankNet](http://research.microsoft.com/en-us/um/people/cburges/papers/ICML_ranking.pdf)
[ReasoNet](https://arxiv.org/abs/1609.05284)
[ReconNet](https://arxiv.org/abs/1601.06892)
[Refine net (DenseBox)](https://arxiv.org/abs/1509.04874)
[RefineNet (Global-Local-Refinement)](https://www.cityscapes-dataset.com/method-details/?submissionID=326)
[RefineNet (Iterative Refinement)](http://cvrr.ucsd.edu/publications/2016/0201.pdf)
[RefineNet (Multipath-RefineNet)](https://arxiv.org/abs/1611.06612)
[ReNet](https://arxiv.org/abs/1505.00393)
[ResNet](https://arxiv.org/abs/1512.03385)
[ResNet (pre-act-ResNet)](https://arxiv.org/abs/1603.05027)
[RetiNet](https://arxiv.org/abs/1610.03628)
[Robust Deep RankNet](https://arxiv.org/abs/1605.04850)
[RotationNet](https://arxiv.org/abs/1603.06208)
[SANet](https://arxiv.org/abs/1611.06878)
[ScatNet](https://arxiv.org/abs/1203.1513)
[SceneFlowNet](https://arxiv.org/abs/1512.02134)
[SceneNet](https://arxiv.org/abs/1511.07041)
[ScenePoseNet](https://arxiv.org/abs/1612.05234)
[SegNet](https://arxiv.org/abs/1511.00561)
[SegNet (Bayesian SegNet)](https://arxiv.org/abs/1511.02680)
[SegResNet](http://link.springer.com/chapter/10.1007%2F978-981-10-3005-5_4)
[shuttleNet](https://arxiv.org/abs/1611.05216)
[SimNet](https://arxiv.org/abs/1506.03059)
[Sketch-a-Net](https://arxiv.org/abs/1501.07873)
[SketchNet](http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Zhang_SketchNet_Sketch_Classification_CVPR_2016_paper.pdf)
[SKYNET](https://arxiv.org/abs/1309.0790)
[SoundNet](https://arxiv.org/abs/1610.09001)
[SPPnet](https://arxiv.org/abs/1406.4729)
[SPyNet](https://arxiv.org/abs/1611.00850)
[SqueezeNet](https://github.com/DeepScale/SqueezeNet)
[SRDANet](https://www.researchgate.net/profile/Lei_Tian22/publication/300569831_SRDANet_An_Efficient_Deep_Learning_Algorithm_for_Face_Analysis/links/577b4aab08ae355e74f079fd.pdf)
[STGConvNet](https://arxiv.org/abs/1606.00972)
[StochasticNet](https://arxiv.org/abs/1508.05463)
[StuffNet](https://arxiv.org/abs/1610.05861)
[StyleNet](https://arxiv.org/abs/1508.06576)
[SyntaxNet](https://arxiv.org/abs/1603.06042)
[ToolNet (EndoNet)](https://arxiv.org/abs/1602.03012)
[TransferNet](https://arxiv.org/abs/1512.07928)
[TristouNet](https://arxiv.org/abs/1609.04301)
[TWSNet](http://www.cvlibs.net/datasets/kitti/eval_object_detail.php?result=78521c3643628e395f57dcbe831fc46dcce0c91f)
[UberNet](https://arxiv.org/abs/1609.02132)
[Unary-Net (FeatMap-Net)](https://arxiv.org/abs/1504.01013)
[UpsampleNet (ECT)](https://arxiv.org/abs/1611.10152)
[VGGNet](https://arxiv.org/abs/1409.1556)
[VIBIKNet](https://arxiv.org/abs/1612.03628)
[VIPLFaceNet](https://arxiv.org/abs/1609.03892)
[VoxNet](http://www.dimatura.net/pages/3dcnn.html)
[VoxResNet](https://arxiv.org/abs/1608.05895)
[WarpNet](https://arxiv.org/abs/1604.05592)
[WaveNet](https://arxiv.org/abs/1609.03499)
[Wide ResNet (WRN)](https://arxiv.org/abs/1605.07146)
[WResNet (Weighted ResNet)](https://arxiv.org/abs/1605.08831)
[XNOR-Net](https://arxiv.org/abs/1603.05279)
[ZFNet](https://arxiv.org/abs/1311.2901)


##Dataset/Benchmark
[ActivityNet](http://activity-net.org/)
[CDnet (ChangeDetection.net)](http://changedetection.net/)
[EmotioNet](http://cbcsl.ece.ohio-state.edu/EmotionNetChallenge/index.html)
[HandNet](http://www.cs.technion.ac.il/~twerd/HandNet/)
[ImageNet](http://image-net.org/)
[ModelNet](http://3dshapenets.cs.princeton.edu/)
[MusicNet](http://homes.cs.washington.edu/~thickstn/musicnet.html)
[ObjectNet3D](http://cvgl.stanford.edu/projects/objectnet3d/)
[SceneNet](http://robotvault.bitbucket.org/)
[SceneNet RGB-D](https://arxiv.org/abs/1612.05079)


##Deep Learning Framework
[Darknet](http://pjreddie.com/darknet/)
[DyNet](https://github.com/clab/dynet)
[LightNet](https://github.com/yechengxi/lightnet)
[MatConvNet](http://www.vlfeat.org/matconvnet/)
[MXNet](http://mxnet.io/)
[RecNet](https://github.com/joergfranke/recnet)
[SparkNet](https://github.com/amplab/SparkNet)
[torchnet](https://github.com/torchnet/torchnet)


##GitHub/Misc
[AgentNet](https://github.com/yandexdataschool/AgentNet)
[CaffeNet](https://github.com/BVLC/caffe/tree/master/models/bvlc_reference_caffenet)
[colornet](https://github.com/pavelgonchar/colornet)
[EmojiNet](https://emojini.curalate.com/)
[EmojiNet](https://arxiv.org/abs/1610.07710)
[ResNeXt](https://arxiv.org/abs/1611.05431)
[SEGNET](https://arxiv.org/abs/1209.6262)
[StereoConvNet](https://github.com/LouisFoucard/StereoConvNet)


## おわりに
~~レッドオーシャン過ぎて途中から目眩が……。~~
取り留めの無いまとめではありますが、「こんな凄いネットワーク思い付きました選手権」に出場する際のネーミングや、サーベイの一助となれば幸いです。

（この記事は[Deep Learning Advent Calendar 2016](http://www.adventar.org/calendars/1687) 17日目の記事です。）