convolutional neural networks のトレンド @wbaflカジュアルトーク#2

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  • #2 (2016.2.7)

    Convolutional Neural Networks

  • (SHIMADA Daiki)@sheema_sheema (Twitter)

    M1

    (!!)

    20142

    1

  • l CNN:

    l CNN 26 !!

    l ??

    l

    l CNN

    Convolutional Neural Networks (CNN)

    2

  • #2Convolutional Neural Networks1. CNN / 2. / 3. 4. 5. 3D6. 7. 8. 9. CNN10. Whats Next ? ImageNet ...

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  • #2Convolutional Neural Networks1. CNN / 2. / 3. 4. 5. 3D6. 7. 8. 9. CNN10. Whats Next ? ImageNet ...

    4

  • CNN

    l

    l 2

    Neocognitron (1980) [1]

    5

    [1] K. Fukushima. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics 36, 1980.

    l

    l Back Propagation(BP)

    LeNet (1998) [2]

    [2] Y LeCun, L Bottou, Y Bengio, P Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE 86, 1998.

  • CNN ,,

    l CNN

    Ave./Max Pooling, Local Contrast Normalization (2009) [3]

    6

    [3] K. Jarrett, K. Kavukcuoglu, M. Ranzato, Y. LeCun. What is the best multi-stage architecture for object recognition?. CVPR, 2009.

    l

    ReLU (2011) [4]

    [4] X. Glorot, A. Bordes, Y. Bengio. Deep Sparse Rectifier Neural Networks. AISTATS 11, 2011.

    l

    Dropout (2012) [5]

    [5] G. E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, R. R. Salakhutdinov. Improving neural networks by preventing co-adaptation of feature detectors. arXiv: 1207.0580, 2012.

  • CNN

    l

    l Data Augmentation (8)

    AlexNet (2012) [6]

    7

    [6] A. Krizhevsky, I. Sutskever, G. E. Hinton. ImageNet Classification with Deep Convolutional Neural Networks. NIPS, 2012.

    l

    l (global ave. pooling)

    Network in Network, global ave. pooling (2013) [7]

    [7] M. Lin, Q. Chen, S. Yan. Network In Network. arXiv: 1312.4400, 2013.

  • CNN

    l 19

    l (3x3)

    VGG-Net (2014) [8]

    8

    [8] K. Simonyan, A. Zisserman. Very Deep Convolutional Networks for Large-Scale Visual Recognition. arXiv: 1409.1556, 2014.

    l 22

    l auxiliary classifiers , Inception module

    GoogLeNet / Inception (2014 ~ 2015) [9, 10]

    [9] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, A. Rabinovich. Going deeper with convolutions. arXiv: 1409.4842, 2014.

    [10] C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, Z. Wojna. Rethinking the Inception Architecture for Computer Vision. arXiv: 1512.00567, 2015.

  • CNN

    l

    l CNN

    SPP-Net (2014) [11]

    9

    [11] K. He, X. Zhang, S. Ren, J. Sun. Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition. arXiv: 1406.4729, 2014.

    l 2

    l guided BP

    All Convolutional Net, guided BP (2014) [12]

    [12] J. T. Springenberg, A. Dosovitskiy, T. Brox, M. Riedmiller. Striving for Simplicity: The All Convolutional Net. arXiv: 1412.6806, 2014.

  • CNN

    l Data Augmentation Exemplar CNN (2014) [13]

    10

    [13] A. Dosovitskiy, P. Fischer, J. T. Springenberg, M. Riedmiller, T. Brox. Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks. arXiv: 1406.6909, 2014.

    l CNN,,

    Triplet Network (2014) [14]

    [14] E. Hoffer, N. Ailon. Deep metric learning using Triplet network. arXiv: 1412.6622, 2014.

  • CNN

    l

    l

    Batch Normalization (2015) [15]

    11

    [15] S. Ioffe, C. Szegedy. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv: 1502.03167, 2015.

    l 152

    l

    Residual Network; ResNet (2015) [16]

    [16] K. He, X. Zhang, S. Ren, J. Sun. Deep Residual Learning for Image Recognition. arXiv: 1512.03385, 2015.

  • AdaGrad [17]

    RMSProp [18]

    AdaDelta [19]

    Adam [20]

    12

    [17] J. Duchi, E. Hazan, Y. Singer. Adaptive Subgradient Methods for Online Learning and Stochastic Optimization. Journal of Machine Learning Research 12 ,2011.

    l (AdaGrad)

    l

    [18] T. Tieleman, G. Hinton. Divide the gradient by a run- ning average of its recent magnitude. COURSERA: Neural Networks for Machine Learning 4, 2012.[19] M. D. Zeiler. ADADELTA: An Adaptive Learning Rate Method. arXiv: 1212.5701, 2012.[20] D. Kingma, J. Ba. Adam: A Method for Stochastic Optimization. arXiv: 1412.6980, 2014.

  • #2Convolutional Neural Networks1. CNN / 2. / 3. 4. 5. 3D6. 7. 8. 9. CNN10. Whats Next ? ImageNet ...

    13

  • CNN /

    l DeconvolutionUnpooling

    Deconvnet for visualizing

    14

    [21] M.D. Zeiler, and R. Fergus. Visualizing and understanding convolutional networks. arXiv,: 1311.2901, 2013.

  • CNN /

    l

    15

    [22] A. Mahendran, A. Vedaldi. Understanding Deep Image Representations by Inverting Them. arXiv: 1412.0035, 2014.

  • CNN /

    l CNN

    l Adversarial example

    CNN

    16

    [24] I. J. Goodfellow, J. Shlens, C. Szegedy. Explaining and Harnessing Adversarial Examples. arXiv: 1412.6572, 2014.

    ostrich !! ostrich !!

    [23] C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. J. Goodfellow, R. Fergus. Intriguing properties of neural networks. arXiv: 1312.6199, 2013.

  • CNN /

    l

    CNN

    17

    [25] A. Nguyen, J. Yosinski, J. Clune. Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images. arXiv: 1412.1897, 2014.

  • #2Convolutional Neural Networks1. CNN / 2. / 3. 4. 5. 3D6. 7. 8. 9. CNN10. Whats Next ? ImageNet ...

    18

  • l CVCNN

    R-CNN (2013)

    19

    [26] R. Girshick, J. Donahue, T. Darrell, J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. arXiv:1311.2524, 2013.

  • l 1 ()

    l CNNROI (ROI Pooling)

    l CV

    Fast R-CNN (2015/4)

    20

    [27] R. Girshick. Fast R-CNN. arXiv:1504.08083, 2015.

  • l CNN (Region Proposal Net)

    Faster R-CNN (2015/6)

    21

    [28] S. Ren, K. He, R. Girshick, J. Sun. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. arXiv:1506.01497, 2015.

  • l CNN

    l Deconvolution

    Fully Convolutional Networks (FCN)

    22

    [29] K. Simonyan, A. Vedaldi, A. Zisserman. Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps. arXiv: 1312.6034, 2013.

  • l Pooling,

    SegNet

    23

    [30] V. Badrinarayanan, A. Handa, R. Cipolla. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Robust Semantic Pixel-Wise Labelling. arXiv: 1505.07293, 2015.

  • l CRF

    l CRFRNN(CRF-RNN)CNNCRF

    CNN + (CRF)

    24

    [31] S. Zheng, S. Jayasumana, B. R. Paredes, V. Vineet, Z. Su, D. Du, C. Huang, P. H. S. Torr. Conditional Random Fields as Recurrent Neural Networks. arXiv: 1502.03240, 2015.

  • l /

    l

    Deep Mask

    25

    [32] P. O. Pinheiro, R. Collobert, P. Dollar. Learning to Segment Object Candidates. arXiv: 1506.06204, 2015.

  • l 3, CNN

    l

    Deep Face

    26

    [33] Y. Taigman, M. Yang, M. A. Ranzato and L. Wolf. DeepFace: Closing the Gap to Human-Level Performance in Face Verification. CVPR, 2014.

  • l

    Spatial Transformer Networks

    27

    [34] M. Jaderberg, K. Simonyan, A. Zisserman, K. Kavukcuoglu. Spatial Transformer Networks. arXiv: 1506.02025, 2015.

  • #2Convolutional Neural Networks1. CNN / 2. / 3. 4. 5. 3D6. 7. 8. 9. CNN10. Whats Next ? ImageNet ...

    28

  • l CNN

    Deep Dream

    29

    [36] K. Simonyan, A. Vedaldi, A. Zisserman. Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps. arXiv: 1312.6034, 2013.

    [35] Inceptionism: Going Deeper into Neural Networks. http://googleresearch.blogspot.ch/2015/06/inceptionism-going-deeper-into-neural.html

  • l 3D

    30

    [37] A. Dosovitskiy, J. T. Springenberg, M. Tatarchenko, T. Brox. Learning to Generate Chairs, Tables and Cars with Convolutional Networks. arXiv: 1411.5928, 2014.

  • l CNN

    31

    [38] L. A. Gatys, A. S. Ecker, M. Bethge. A Neural Algorithm of Artistic Style. arXiv: 1508.06576, 2015.

    1

    5

  • l CNNMRF

    32

    [39] C. Li, M. Wand. Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis. arXiv:1601.04589, 2016.

  • l Adversarial Networks

    DCGAN

    33

    [40] A. Radford, L. Metz, S. Chintala. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. arXiv:1511.06434, 2015.

  • l waifu2x[42]

    Super-Resolution CNN (SRCNN)

    34

    [41] C. Dong, C. C. Loy, K. He, X. Tang. Image Super-Resolution Using Deep Convolutional Networks. arXiv:1501.00092, 2015.

    [42] waifu2x. http://waifu2x.udp.jp/index.ja.html

  • l CNNmotion kernelMRF

    Deblurring ()

    35

    [43] J. Sun, W. Cao, Z. Xu, J. Ponce. Learning a Convolutional Neural Network for Non-uniform Motion Blur Removal. arXiv:1503.00593, 2015.

  • l hypercolumns [45]

    Automatic Colorization CNN

    36

    [44] Automatic Colorization, http://tinyclouds.org/colorize/

    [45] B. Hariharan, P. Arbelez, R. Girshick, J. Malik. Hypercolumns for Object Segmentation and Fine-grained Localization. arXiv: 1411.5752, 2014.

    original CNN human(Reddit)

  • #2Convolutional Neural Networks1. CNN / 2. / 3. 4. 5. 3D6. 7. 8. 9. CNN10. Whats Next ? ImageNet ...

    37

  • 3D

    l Selection Tower (depth)Color Tower () 2

    Deep Stereo

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