Читать книгу A Guide to Convolutional Neural Networks for Computer Vision - Salman Khan - Страница 8
ОглавлениеContents
1.1.2 Image Processing vs. Computer Vision
2.1 Importance of Features and Classifiers
2.2 Traditional Feature Descriptors
2.2.1 Histogram of Oriented Gradients (HOG)
2.2.2 Scale-invariant Feature Transform (SIFT)
2.2.3 Speeded-up Robust Features (SURF)
2.2.4 Limitations of Traditional Hand-engineered Features
2.3 Machine Learning Classifiers
2.3.1 Support Vector Machine (SVM)
3.3.2 Parameter Learning
3.4 Link with Biological Vision
3.4.1 Biological Neuron
3.4.2 Computational Model of a Neuron
3.4.3 Artificial vs. Biological Neuron
4 Convolutional Neural Network
4.2 Network Layers
4.2.1 Pre-processing
4.2.2 Convolutional Layers
4.2.3 Pooling Layers
4.2.4 Nonlinearity
4.2.5 Fully Connected Layers
4.2.6 Transposed Convolution Layer
4.2.7 Region of Interest Pooling
4.2.8 Spatial Pyramid Pooling Layer
4.2.9 Vector of Locally Aggregated Descriptors Layer
4.2.10 Spatial Transformer Layer
4.3 CNN Loss Functions
4.3.1 Cross-entropy Loss
4.3.2 SVM Hinge Loss
4.3.3 Squared Hinge Loss
4.3.4 Euclidean Loss
4.3.5 The ℓ1 Error
4.3.6 Contrastive Loss
4.3.7 Expectation Loss
4.3.8 Structural Similarity Measure
5.1 Weight Initialization
5.1.1 Gaussian Random Initialization
5.1.2 Uniform Random Initialization
5.1.3 Orthogonal Random Initialization
5.1.4 Unsupervised Pre-training
5.1.5 Xavier Initialization
5.1.6 ReLU Aware Scaled Initialization
5.1.7 Layer-sequential Unit Variance
5.1.8 Supervised Pre-training
5.2 Regularization of CNN
5.2.1 Data Augmentation
5.2.2 Dropout
5.2.3 Drop-connect
5.2.4 Batch Normalization
5.2.5 Ensemble Model Averaging
5.2.6 The ℓ2 Regularization
5.2.7 The ℓ1 Regularization
5.2.8 Elastic Net Regularization
5.2.9 Max-norm Constraints
5.2.10 Early Stopping
5.3 Gradient-based CNN Learning
5.3.1 Batch Gradient Descent
5.3.2 Stochastic Gradient Descent
5.3.3 Mini-batch Gradient Descent
5.4 Neural Network Optimizers
5.4.1 Momentum
5.4.2 Nesterov Momentum
5.4.3 Adaptive Gradient
5.4.4 Adaptive Delta
5.4.5 RMSprop
5.4.6 Adaptive Moment Estimation
5.5 Gradient Computation in CNNs
5.5.1 Analytical Differentiation
5.5.2 Numerical Differentiation
5.5.3 Symbolic Differentiation
5.5.4 Automatic Differentiation
5.6 Understanding CNN through Visualization
5.6.1 Visualizing Learned Weights
5.6.2 Visualizing Activations
5.6.3 Visualizations based on Gradients
6 Examples of CNN Architectures
6.1 LeNet
6.2 AlexNet
6.3 Network in Network
6.4 VGGnet
6.5 GoogleNet
6.6 ResNet
6.7 ResNeXt
6.8 FractalNet
6.9 DenseNet
7 Applications of CNNs in Computer Vision
7.1 Image Classification
7.1.1 PointNet
7.2 Object Detection and Localization
7.2.1 Region-based CNN
7.2.2 Fast R-CNN
7.2.3 Regional Proposal Network (RPN)
7.3 Semantic Segmentation
7.3.1 Fully Convolutional Network (FCN)
7.3.2 Deep Deconvolution Network (DDN)
7.3.3 DeepLab
7.4 Scene Understanding
7.4.1 DeepContext
7.4.2 Learning Rich Features from RGB-D Images
7.4.3 PointNet for Scene Understanding
7.5 Image Generation
7.5.1 Generative Adversarial Networks (GANs)
7.5.2 Deep Convolutional Generative Adversarial Networks (DCGANs)
7.5.3 Super Resolution Generative Adversarial Network (SRGAN)
7.6 Video-based Action Recognition
7.6.1 Action Recognition From Still Video Frames
7.6.2 Two-stream CNNs
7.6.3 Long-term Recurrent Convolutional Network (LRCN)
8 Deep Learning Tools and Libraries
8.1 Caffe
8.2 TensorFlow
8.3 MatConvNet
8.4 Torch7
8.5 Theano
8.6 Keras
8.7 Lasagne
8.8 Marvin
8.9 Chainer
8.10 PyTorch
