Lecture 6 Deep Learning and Computer Vision.
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1 Lecture 6 Deep Learning and Computer Vision peimt@bit.edu.cn 1
2 Deep Learning slides of Xin Liu of vipl.ict.ac.cn 2
3 Deep Learning
4 Deep Learning
5 Deep Learning
6 Deep Learning
7 Deep Learning
8 Deep Learning
9 Traditional Computer Vision
10 Human Brain v Humans have a primary visual cortex, also known as V1, containing 140 million neurons, with tens of billions of connections between them. v Human vision involves not just V1, but an entire series of visual cortices - V2, V3, V4, and V5 - doing progressively more complex image processing.
11 Human Brain
12 Human Brain v The difficulty of visual pattern recognition becomes apparent if you attempt to write a computer program to recognize digits like those above. v When you try to make such rules precise, you quickly get lost in a morass of exceptions and caveats and special cases. It seems hopeless.
13 Neural Networks v Neural networks approach the problem in a different way. v Take a large number of handwritten digits, known as training examples v Develop a system which can learn from those training examples v Uses the examples to automatically infer rules for recognizing handwritten digits
14 Perceptrons v A perceptron takes several inputs, and produces a single binary output:
15 Perceptrons v Perceptron can weigh up different kinds of evidence in order to make decisions v A complex network of perceptrons could make quite subtle decisions
16 Perceptrons
17 Perceptrons
18 Neural Networks
19 Neural Networks v If it were true that a small change in a weight (or bias) causes only a small change in output, then we could use this fact to modify the weights and biases to get our network to behave more in the manner we want. v Changing the weights and biases over and over to produce better and better output.
20 Sigmoid neuron
21 Sigmoid neuron
22 Neural Networks v By using the activation function we get a smoothed out perceptron. v The smoothness means that small changes in the weights and in the bias will produce a small change in the output from the neuron
23 Multilayer Perceptrons
24 Multilayer Perceptrons
25 Multilayer Perceptrons
26 Multilayer Perceptrons
27 Quadratic cost function
28 Quadratic cost function v For some function
29 Quadratic cost function
30 Quadratic cost function
31 Stochastic gradient descent v Estimate the gradient by computing for a small sample of randomly chosen training inputs (mini batch). v By averaging over this small sample it turns out that we can quickly get a good estimate of the true gradient and this helps speed up gradient descent, and thus learning.
32 Why CNN v For input as a 10 * 10 image: - A 3 layer MLP with 200 hidden units and 10 output units contains ~22k parameters v For input as a 100 * 100 image: - A 3 layer MLP with 20k hidden units and 10 output units contains ~200m parameters
33 Why CNN v MLP can be improved in two ways: - Locally connected instead of fully connected - Sharing weights between neurons v We achieve those by using convolution neurons
34 Local receptive fields
35 Local receptive fields stride length is 1
36 Shared weights and biases v Each hidden neuron has a bias and 5 5 weights connected to its local receptive field. v Use the same weights and bias for each of the hidden neurons
37 Shared weights and biases
38 Pooling layers
39 Pooling layers
40 Pooling layers
41 Fully-connected layer
42 Deep Learning The traditional method:hand-craft feature+classifier The modern method:unsupervised mid-level feature learning Deep learning:end to end hierarchal feature learning
43 Deep Learning
44 Understand the Human Brain
45 Understand the Human Brain
46 Understand the Human Brain
47 Understand the Human Brain
48 Neural Network: concatenation of functions
49 Neural Network: concatenation of functions
50 Activation Functions
51 Loss Functions v Euclidean Loss v Cross-entropy loss v Contrastive Loss v Triplet Loss v Moon Loss
52 Why does CNN work v Faster heterogeneous parallel computing CPU clusters, GPUs, etc. v Large dataset ImageNet: 1.2m images of 1,000 object classes CoCo: 300k images of 2m object instances v Improvements in model architecture ReLU, dropout, inception, etc.
53 Case Study: LeNet-5
54 Case Study: ResNet
55 Case Study: ResNet
56 Other Deep models-siamese Net
57 Other Deep models-c3d
58 Other Deep models-rnn
59 Other Deep models-lstm
60 Deep Learning in Face Recognition 60
61 DeepID Sun Y, et al CVPR 2014
62 DeepID Sun Y, et al CVPR 2014
63 DeepID2 Sun Y, et al NIPS 2014
64 DeepID2+ Sun Y, et al CVPR 2015
65 DeepID3 Sun Y, et al arxiv 2015
66 DeepFace Yaniv Taigman, et al CVPR 2014
67 FaceNet Florian Schroff, et al CVPR 2015
68 Deep Learning in Face Recognition Slide from Xin Liu VIPL
69 Deep Learning in Object Detection 69
70 R-CNN Girshick, CVPR 2014
71 SPP-Net K He, et al, ECCV 2014
72 Fast R-CNN Girshick, ICCV 2015
73 Faster R-CNN Girshick, NIPS 2015
74 YOLO: You Only Look Once Redmon J, et al, arxiv 2015
75 SSD: Single Shot MultiBox Detector Wei Liu, et al, ECCV 2016
76 Deep Learning in Object Detection Slide from Xin Liu VIPL
77 Deep Learning in Image Classification Slide from Xin Liu VIPL
78 Deep Learning in Face Retrieval 78
79 Deep CNN based Binary Hash Video Representations Zhen Dong, et al, AAAI 2016
80 Deep Learning in Object Tracking 80
81 DeepTrack Hanxi Li, et al, TIP 2016
82 82
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