DL Tutorial. Xudong Cao
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1 DL Tutorial Xudong Cao
2 Historical Line 1960s Perceptron 1980s MLP BP algorithm 2006 RBM unsupervised learning 2012 AlexNet ImageNet Comp GoogleNet VGGNet ImageNet Comp. Rule based AI algorithm Game Tree & Search Algorithm Support vector machine Wrong direction Big booming: DNNResearch 6M, DeepMind 400M Google, Microsoft, Baidu, FB, Apple Hundreds of Startups
3 Linear inseparable problem & fitting power
4 Solution 1: Going High dimension Implicitly project to high dim. Explicitly design high-dim. features e.g. high-dim LBP and fisher vector
5 Solution 2: Going Deep
6 High-Dim VS. Deep High-Dim Easy to train, convex in general Solid mathematic foundation Generalized well Low computational cost Deep Hard to train, non-convex Black magic & unknown territory Prone to over-fitting High computational cost Fitting power scales linearly Fitting power scales exponentially
7 Explains why people hated neural networks in the past, BUT time changes
8 New Era: Big Data & Moore s Rule
9 Practical application Xiaogang Wang, Introduction to Deep Learning
10 End-to-end learning, less domain knowledge Training Training Model Design Networks Design No or very small amount of domain knowledge Conventional Approach Feature Design Deep Learning Small amount of domain knowledge Pre-processing Large amount of domain knowledge Collect Data Collect Data Xiaogang Wang, Introduction to Deep Learning
11 Good features Xiaogang Wang, Introduction to Deep Learning
12 Good features cont. Transfer ImageNet features to other tasks Dataset Oxford 102 Flowers Oxford-IIIT Pets Conv. Best (acc) Tran. Best (acc) 91.3% 98.7% 88.1% 93.1% FGVC-Aircraft 81.5% 85.2% MIT-67 indoor % 82.4% Transfer the face identification features to age estimation & gender classification Human Age Estimation Dataset Pre. Best (MAE) Ours(MAE) Morph FGNet Geneder Classification Dataset Pre. Best (acc) Ours(acc) Morph 98.7% 99.4%
13 Directions of DL Research Feature engineering to architecture engineering ImageNet Classification with Deep Convolutional Neural Networks (Alex Net) Going Deeper with Convolutions (Google Net) Very Deep Convolutional Networks for Large-Scale Visual Recognition (VGG Net) Faster and smaller How to train very deep neural network Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (Speedup CNN training) Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification (Good initialization)
14 Directions of DL Research cont. Existing Applications Face: DeepFace, Deep ID serials & FaceNet Detection: R-CNN, fast R-CNN, faster R-CNN Segmentation: F-CNN serials New applications Image captioning [Google & Berkeley] Synthesize real world images [Facebook AI Lab] A Neural Algorithm of Artistic Style [Gatys et al.]
15 CV Tutorial
16 Computer vision tech. tree taking autonomous driving as an example Traffic sign/light recognition Car type recognition Road/Lane Parsing Unknown obstacle Parsing Parsing Recognition Detection Car/Pred./Lane Detection Traffic sign/light detection Better, faster, smaller model Deep learning infrastructure
17 Image classification and ImageNet Dataset
18 Classifying plankton
19 Face classification and verification Face classification or recognition: who is he/her? Face verification: whether the two are the same person? Learn face representation from classification task
20 From classification to detection and segmentation Image Classification (Per-image) Object Detection (Per-region) Semantic Segmentation (Per-pixel) Shaoqing Ren, Object Detection with Deep Convolutional Network
21 Sliding Window window scans image, categorizing all possible candidates Shaoqing Ren, Object Detection with Deep Convolutional Network
22 Sliding Window another scale/ratio window Shaoqing Ren, Object Detection with Deep Convolutional Network
23 Sliding Window Shaoqing Ren, Object Detection with Deep Convolutional Network
24 Sliding Window Shaoqing Ren, Object Detection with Deep Convolutional Network
25 Fully-convolution network (FCN) Sliding window or Convolution kernel Fully-convolution network functions like Sliding window Shaoqing Ren, Object Detection with Deep Convolutional Network
26 Intelligent Surveillance
27 Autonomous driving
28 Thanks
29
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