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Convolutional Networks
Published on Sep 13, 201516902 Views
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Chapter list
Convolutional Networks00:00
Unsupervised Convolutional Networks00:39
Learning Feature Hierarchy - 100:50
Learning Feature Hierarchy - 201:37
Learning object representations02:21
Illustration: Learning an “eye” detector03:40
Convolutional RBM (CRBM) - 109:51
Convolutional architectures17:52
Convolutional Deep Belief Networks (CDBN) - 118:09
Convolutional Deep Belief Networks (CDBN) - 218:17
Convolutional RBM (CRBM) - 222:27
Inference: probabilistic max-pooling22:42
Unsupervised learning from natural images29:07
Unsupervised learning of object-parts29:08
Convolutional Sparse Coding40:23
Deconvolutional Networks43:00
Supervised Convolutional Networks43:01
Example: Convolutional Neural Networks43:05
Convolutional Neural Networks55:38
Components of Each Layer56:12
Filtering57:04
Non-Linearity57:40
Pooling01:04:20
Normalization01:04:45
Applications01:05:54
Application: ImageNet01:06:23
Krizhevsky et al. [NIPS 2012]01:06:33
ImageNet Classification 201201:08:41
ImageNet Classification 2013 Results - 101:09:19
ImageNet Classification 2013 Results - 201:09:38
Feature Generalization - 101:10:13
Feature Generalization - 301:13:41
Feature Generalization - 401:14:01
Feature generalization over multiple tasks01:14:15
Feature Generalization - 201:14:48
Using very deep layers: VGG Network01:16:18
Going deeper: GoogLeNet - 101:19:39
Going deeper: GoogLeNet - 201:22:56
Experimental results on ILSVRC01:23:16
Other vision applications01:24:03
Object detection using multi-scale CNN01:24:04
Object detection using Convolutional Neural Networks01:25:12
CNN Object detection with Bayesian optimization01:26:32
CNN object detection with structured loss - 101:27:42
CNN object detection with structured loss - 201:28:16
Image segmentation and parsing01:29:36
Other Applications01:30:21
Industry Deployment01:30:39
Deep Visual-Semantic Embedding01:31:12
Multiple output embeddings for zero-shot learning01:31:41