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Su-A Kim 12 th August 2014 Convolutional Neural Networks ConvNet ● ○ ○ ○ ○ ○ ○ ○ ○ ○ DeepFace ○ ○ ○ ○ ○ ○ ○

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Table of contents Introduce Convolutional Neural Networks Introduce application paper : “ DeepFace: Closing the Gap to Human-Level Performance in Face Verification”, CVPR 2014 ConvNet ● ○ ○ ○ ○ ○ ○ ○ ○ ○ DeepFace ○ ○ ○ ○ ○ ○ ○

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Su-A Kim 12 th August 2014 @CVLAB History Yann LeCun In 1995, Yann LeCun and Yoshua Bengio introduced the concept of convolutional neural networks. Yoshua Bengio ConvNet ● ○ ○ ○ ○ ○ ○ ○ ○ ○ DeepFace ○ ○ ○ ○ ○ ○ ○

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Recap of Convnet Neural network with specialized connectivity structure Feed-forward: - Convolve input - Non-linearity (rectified linear) - Pooling (local max) Supervised Train convolutional filters by back-propagating classification error Feature maps Pooling Non-linearity Convolution (Learned) Input image Slide: R.fergus Su-A Kim 12 th August 2014 @CVLAB ConvNet ○ ● ○ ○ ○ ○ ○ ○ ○ ○ DeepFace ○ ○ ○ ○ ○ ○ ○

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Connectivity & weight sharing depends on layer All different weights Convolution layer has much smaller number of parameters by local connection and weight sharing All different weightsShared weights Su-A Kim 12 th August 2014 @CVLAB ConvNet ○ ○ ● ○ ○ ○ ○ ○ ○ ○ DeepFace ○ ○ ○ ○ ○ ○ ○

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features Convolution layer Detect the same feature at different positions in the input image Filter (kernel) Input Feature map Slide: R.fergus Su-A Kim 12 th August 2014 @CVLAB ConvNet ○ ○ ○ ● ○ ○ ○ ○ ○ ○ DeepFace ○ ○ ○ ○ ○ ○ ○

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Non-linearity Tanh Sigmoid: 1/(1+exp(-x)) Rectified linear (ReLU) : max(0,x) - Simplifies backprop - Makes learning faster - Make feature sparse → Preferred option Slide: R.fergus Su-A Kim 12 th August 2014 @CVLAB ConvNet ○ ○ ○ ○ ● ○ ○ ○ ○ ○ DeepFace ○ ○ ○ ○ ○ ○ ○

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Sub-sampling layer Spatial Pooling - Average or Max - Boureau et al. ICML’10 for theoretical analysis → Max 가 더 좋다는 연구 Role of Pooling - Invariance to small transformations - reduce the effect of noises and shift or distortion Slide: R.fergus Max Sum Su-A Kim 12 th August 2014 @CVLAB ConvNet ○ ○ ○ ○ ○ ● ○ ○ ○ ○ DeepFace ○ ○ ○ ○ ○ ○ ○

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Normalization Contrast normalization (between/across feature map) - Equalizes the features map → Detail 하지 않은 feature 를 잡아냄 Feature mapsFeature maps after contrast normalization Slide: R.fergus Su-A Kim 12 th August 2014 @CVLAB ConvNet ○ ○ ○ ○ ○ ○ ● ○ ○ ○ DeepFace ○ ○ ○ ○ ○ ○ ○

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LeNet 5 C1,C3,C5 : Convolutional layer. (5 × 5 Convolution matrix.) S2, S4 : Subsampling layer. (by factor 2) F6 : Fully connected layer. About 187,000 connection. About 14,000 trainable weight. Su-A Kim 12 th August 2014 @CVLAB ConvNet ○ ○ ○ ○ ○ ○ ○ ● ○ ○ DeepFace ○ ○ ○ ○ ○ ○ ○

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LeNet 5 노이즈에도 강건 Su-A Kim 12 th August 2014 @CVLAB ConvNet ○ ○ ○ ○ ○ ○ ○ ○ ● ○ DeepFace ○ ○ ○ ○ ○ ○ ○

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About CNN’s A special kind of multi-layer neural networks. Implicitly extract relevant features. A feed-forward network that can extract topological properties from an image. Like almost every other neural networks CNNs are trained with a version of the back-propagation algorithm. Su-A Kim 12 th August 2014 @CVLAB ConvNet ○ ○ ○ ○ ○ ○ ○ ○ ○ ● DeepFace ○ ○ ○ ○ ○ ○ ○

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Yaniv Taigman, Ming Yang, Marc’ Aurelio Ranzato, Lior Wolf Facebook AI Research, Tel Aviv University DeepFace: Closing the Gap to Human-Level Performance in Face Verification Reach an accuracy of 97.35% Su-A Kim 12 th August 2014 @CVLAB ConvNet ○ ○ ○ ○ ○ DeepFace ● ○ ○ ○ ○ ○ ○

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Architecture Face Alignment Representation(CNN) Su-A Kim 12 th August 2014 @CVLAB ConvNet ○ ○ ○ ○ ○ DeepFace ● ○ ○ ○ ○ ○ ○

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Face Alignment (1) 2D alignment (2) 3D alignment 얼굴 영역 검출 후, 기준점 6 개 추출 기준점 추출 : LBP histogram 을 descriptor 로 사용해서 미리 학습된 SVR(Support Vector Regressor) 로 추출 67 개 landmark Landmark mapping 2D-3D alignFrontalization2D projection Su-A Kim 12 th August 2014 @CVLAB ConvNet ○ ○ ○ ○ ○ DeepFace ○ ● ○ ○ ○ ○ ○

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Representation C1-M2-C3 Low-level feature 추출 (simple edges and texture) Apply max-pooling only to the first convolution layer, why? Input 152x152 Su-A Kim 12 th August 2014 @CVLAB ConvNet ○ ○ ○ ○ ○ DeepFace ○ ○ ● ○ ○ ○ ○

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Representation L4-L5-L6 (Locally connected) 152x152 Locally connected layer 를 사용한 이유 ? : 각각의 영역들은 서로 다른 local statistic 을 가짐 All different weights Shared weights Su-A Kim 12 th August 2014 @CVLAB ConvNet ○ ○ ○ ○ ○ DeepFace ○ ○ ○ ● ○ ○ ○ Low-level feature 추출 (simple edges and texture) Apply max-pooling only to the first convolution layer, why? C1-M2-C3

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Representation L4-L5-L6 (Locally connected) F7-F8 (Fully connected) Low-level feature 추출 (simple edges and texture) Apply max-pooling only to the first convolution layer, why? 152x152 얼굴에서 떨어져 있는 부분에서 뽑힌 feature 사이의 correlation 을 구할 수 있음 Output of F7 : raw face representation feature vector Output of F8 : Class labels 의 확률분포를 구하는데 사용됨 Locally connected layer 를 사용한 이유 ? Su-A Kim 12 th August 2014 @CVLAB ConvNet ○ ○ ○ ○ ○ DeepFace ○ ○ ○ ○ ● ○ ○ C1-M2-C3

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Training Correct class 의 확률을 최대화 하는 것이 목적 Back-propagation 해서 파라미터를 최소화하고, stochastic gradient descent(SGD) 를 사용해서 파라미터를 업데이트 Su-A Kim 12 th August 2014 @CVLAB ConvNet ○ ○ ○ ○ ○ DeepFace ○ ○ ○ ○ ○ ● ○

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Result Reduces the error of the previous best methods by more than 50% Youtube 에 100 개정도 잘못 라벨링 된 것들이 있어서 그것까지 치면 92.5% 정도 됨 Su-A Kim 12 th August 2014 @CVLAB ConvNet ○ ○ ○ ○ ○ DeepFace ○ ○ ○ ○ ○ ○ ●

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Reference [1] Bouchain, David. "Character recognition using convolutional neural networks.“ Institute for Neural Information Processing 2007 (2006). [2] Bouvrie, Jake. "Notes on convolutional neural networks." (2006). [3] Glorot, Xavier, Antoine Bordes, and Yoshua Bengio. "Deep sparse rectifier networks." Proceedings of the 14th International Conference on Artificial Intelligence and Statistics. JMLR W&CP Volume. Vol. 15. 2011. [4] Ahonen, Timo, Abdenour Hadid, and Matti Pietikainen. "Face description with local binary patterns: Application to face recognition." Pattern Analysis and Machine Intelligence, IEEE Transactions on 28.12 (2006): 2037-2041. [5] Bengio, Yoshua. "Learning deep architectures for AI." Foundations and trends® in Machine Learning 2.1 (2009): 1-127. Su-A Kim 12 th August 2014 @CVLAB ConvNet ○ ○ ○ ○ ○ DeepFace ○ ○ ○ ○ ○ ○ ●

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