CSE 802 Spring Deep Learning

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1 CSE 802 Spring 2017 Deep Learning Inci M. Baytas Michigan State University February 13-15,

2 Deep Learning in Computer Vision Large-scale Video Classification with Convolutional Neural Networks, CVPR

3 Deep Learning in Computer Vision Microsoft Deep Learning Semantic Image Segmentation 3

4 Deep Learning in Computer Vision NeuralTalk and Walk, recognition, text description of the image while walking. 4

5 Deep Learning in Robotics Self Driving Cars 5

6 Deep Learning in Robotics Deep Sensimotor Learning 6

7 Other Applications of Deep Learning Natural Language Processing (NLP) Speech recognition and machine translation Why Should We Be Impressed? Automated vision (e.g., object recognition) is challenging: different viewpoints, scales, occlusions, illumination, Robotics (e.g., autonomous driving) in real life environments (constantly changing, new tasks without guidance, unexpected factors) is challenging NLP (e.g., understanding human conversations) is an extremely complex task: noise, context, partial sentences, different accent,.. 7

8 Why Is Deep Learning So Popular Now? Better hardware Bigger data Regularization methods (dropout) Variety of optimization methods SGD Adagrad Adadelta ADAM RMS Prop 8

9 Criticism and Limitations of Deep Networks Large amount of data required for training High performance computing a necessity Non-optimal method Task specific Lack of theoretical understanding 9

10 Common Deep Network Types Feed forward networks Convolutional neural networks Recurrent neural networks 10

11 Components of Deep Learning Loss functions Squared loss: (y - f(x))2 Logistic loss: log(1 + e-yf(x)) Hinge loss: (1 + yf(x))+ Squared hinge loss: (1 + yf(x))+2 Non-linear activation functions Linear Tanh Sigmoid Softmax ReLU 11

12 12

13 Components of Deep Learning Optimizers Gradient Descent Adagrad (Adaptive Gradient Algorithm) Adadelta (An Adaptive Learning Rate Method) ADAM (Adaptive Moment Estimation) RMSProp Regularization Methods L2 norm L1 norm Dataset Augmentation Noise robustness Early stopping Dropout [12] 13

14 Components of Deep Learning Number of iterations Less iterations: may underfitting More iterations: use a stopping criteria Step size Very large step size: may miss optimal point Very small step size: takes longer to converge Parameter Initialization Initializing with zeros Random initialization Xavier initialization 14

15 Components of Deep Learning Batch size Bigger batch size: might require less iterations Smaller batch size: will need more iterations Number of layers More layers (more depth): introducing more non-linearity, more complexity, more parameters Too many layers might cause overfitting. Number of hidden parameters Large number of hidden layer: more model complexity, can approximate a more complex classifier Too many parameters: overfitting, increased training time 15

16 Convolutional Neural Networks Convolutional networks are simply neural networks that use convolution in place of general matrix multiplication in at least one of their layers [1]. Convolution: A linear operator Cross-correlation with a flipped kernel. Convolution in spatial domain corresponds to multiplication in frequency domain. 16

17 Convolutional Neural Networks (CNNs) Feed forward networks that can extract topological features from images. Can provide invariance to geometric distortions such as translation, scaling, and rotation. Hierarchical and robust feature extraction was done before CNNs. CNN is data-driven. Parameters of filters are learned from the data instead of predefined values. At each iteration, parameters are updated to minimize the loss. 17

18 Convolution Layer Local (sparse) connectivity Reduces memory requirements Fewer operations Parameter sharing Same kernel used at every position of the input How to choose the Equivariance filter size? property Receptive field 18

19 Pooling Layer (Subsampling) Convolution stage: several convolutions in parallel to produce a set of linear activations Followed by non-linear activation Then pooling layer: Invariance to small translations Dealing with variable size inputs 19

20 Fully-Connected Layer Maps the latent representation of input to output Output: One-hot representation of class label Predicted response Appropriate activation function, e.g., softmax for classification. 20

21 Feature Extraction with CNNs 21

22 Some Example CNN Architectures LeNet-5 [2] 22

23 Some Example CNN Architectures AlexNet (5 layers) 23

24 Some Example CNN Architectures VGG 16 [3] 24

25 GoogLeNet (22 layers) 25

26 Tricks to Improve CNN Performance Data augmentation Flipping (commonly used in face) Translation Rotation Stretching Normalizing, Whitening (less redundancy) Cropping and alignment (for especially face) 26

27 Project You will implement 11-layer CNN architecture proposed in [6] to extract features. 27

28 Project You can use a deep learning library to implement the network. Library will take care of convolution, pooling, dropout, and back propagation. You need to define cost function and activation functions. The activation function of the output layer is softmax since it is a classification problem. You can use tensorflow. 28

29 HPCC Data and evaluation protocol are on HPCC. /mnt/research/cse_802_spr_17 To connect HPCC: ssh and msu password To run small examples use developer mode: ssh dev-intel14 Try to log in to HPCC and check the course research space. Try to use a python IDE (PyCharm). Debug your code and understand how tensorflow works (if you are not familiar with a deep learning library). 29

30 Casia Dataset (Cropped Images) The database contains 494,414 images. 10,575 subjects in total We provide cropped and original images under /mnt/research/cse_802_spr_17 30

31 Test Data and Evaluation Protocol Final evaluation on Labeled Faces in the Wild (LFW) database [7] with 13,233 images, 5,749 subjects. Evaluation protocol: BLUFR protocol [8]; find under /mnt/research/cse_80 2_SPR_17 31

32 References A. Krizhevsky, I. Sutskever and G. E. Hinton ImageNet Classification with Deep Convolutional Neural Networks, NIPS 2012: Neural Information Processing Systems, Lake Tahoe, Nevada Dong Yi, Zhen Lei, Shengcai Liao and Stan Z. Li. Learning Face Representation from Scratch, arxiv: v1 [cs.cv], Shengcai Liao, Zhen Lei, Dong Yi, Stan Z. Li, "A Benchmark Study of Large-scale Unconstrained Face Recognition." In IAPR/IEEE International Joint Conference on Biometrics, Sep Oct. 2, Clearwater, Florida, USA, Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever and Ruslan Salakhutdinov, Dropout: A Simple Way to Prevent Neural Networks from Overfitting, Journal of Machine Learning Research 15 (2014)

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