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Convolutional Neural Networks and Computer Vision
Published on 2016-08-2317068 Views
This talk will review Convolutional Neural Network models and the tremendous impact they have made on Computer Vision problems in the last few years.
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Presentation
Introduction to Convolutional Networks00:00
Overview00:18
Neural Net00:53
Convolutional Neural Networks01:07
Multistage Hubel-Wiesel Architecture01:35
Overview of Convnets02:39
Convnet Successes03:56
Application to ImageNet04:56
Goal05:49
Krizhevsky et al. [NIPS2012] 06:36
ImageNet Classification (2010 – 2015)07:33
Examples - 108:42
Examples - 209:00
Examples - 309:08
Using Features on Other Datasets09:44
Caltech 256 - 110:00
Caltech 256 - 210:24
The Details10:58
Filtering - 111:51
Filtering - 213:20
Filtering - 314:57
Non-Linearity - 217:30
Non-Linearity - 117:50
Pooling - 118:29
Pooling - 220:35
Role of Pooling20:57
Components of Each Layer23:25
Architecture 23:51
How to Choose Architecture24:16
How important is Depth25:59
Architecture of Krizhevsky et al. - 126:09
Architecture of Krizhevsky et al. - 227:25
Architecture of Krizhevsky et al. - 328:09
Architecture of Krizhevsky et al. - 428:13
Architecture of Krizhevsky et al. - 528:38
Tapping off Features at each Layer29:31
Translation (Vertical)29:52
Scale Invariance31:08
Rotation Invariance31:31
Very Deep Models (2)32:55
GoogLeNet vs Previous Models33:14
Google Inception model33:36
Very Deep Models (1)34:12
Residual Networks34:29
Visualizing Convnets - 137:49
Visualizing Convnets - 238:50
Projection from Higher Layers38:58
Details of Operation39:52
Unpooling Operation40:38
Layer 1 Filters41:22
Visualizations of Higher Layers41:42
Layer 1: Top-9 Patches42:22
Layer 2: Top-142:56
Layer 2: Top-9 Patches46:09
Layer 2: Top-946:30
Visualizing Convnets47:05
Google DeepDream47:47
Training Big ConvNets48:01
Evolution of Features During Training - 149:50
Evolution of Features During Training - 250:14
Normalization across Data50:41
Annealing of Learning Rate52:09
Automatic Tuning of Learning Rate?52:19
Local Minima?53:40
What about 2nd order methods?56:30
Saddle Point Perspective58:25
Improving Generalization59:36
Big Model + Regularize vs Small Model01:00:47
Fooling Convnets01:01:47
DropOut01:02:38
Other things good to know - 201:03:37
Other things good to know - 301:04:10
Other things good to know - 101:04:38
Other things good to know - 401:04:55
What if it does not work?01:05:06
Industry Deployment01:05:47
Labeled Faces in Wild Dataset01:06:23
Detection with ConvNets01:07:25
Two General Approaches01:07:39
Sliding Window with ConvNet01:08:28
Multi-Scale Sliding Window ConvNet - 101:08:45
Multi-Scale Sliding Window ConvNet - 201:09:04
OverFeat – Output before NMS01:09:20
Overfeat Detection Results01:10:06
R-CNN Approach01:10:24
Video Classification01:11:04
Action Recognition Results01:12:28
2D vs 3D Convnets01:12:58
Sport Classification Results01:13:19
Dense Scene Labeling - 101:14:09
Dense Scene Labeling - 201:14:52
Dense Scene Labeling - 301:15:03
Dense Scene Labeling - 401:15:06
Architecture01:15:29
Multi-Scale Convnets01:15:42
Eigen et al. architecture01:15:52
Use Appropriate Loss Functions01:16:07
Depths Comparison01:16:31
Surface Normals01:16:45
Scene Parsing01:16:51
Segmentation01:17:00
Denoising with ConvNets01:18:46
Deblurring with Convnets01:19:10
Inpainting with Convnets01:19:28
Removing Local Corruption01:19:38
Convnet + Structured Learning - 101:20:32
Convnet + Structured Learning - 201:21:12
Body Tracking01:21:16
Body Tracking: Part Detector01:21:48
Body Tracking: Spatial Model - 101:21:54
Body Tracking: Spatial Model - 201:22:23