The idea of feature matching is to add an extra variable to the cost function of the generator in order to penalize the difference between absolute errors in the test data and training data.
Feature matching
by Pradeep Pujari, Md. Rezaul Karim, Mohit Sewak
Practical Convolutional Neural Networks
- Title Page
- Copyright and Credits
- Packt Upsell
- Contributors
- Preface
- Deep Neural Networks – Overview
- Introduction to Convolutional Neural Networks
- Build Your First CNN and Performance Optimization
- CNN architectures and drawbacks of DNNs
- Convolution and pooling operations in TensorFlow
- Training a CNN
- Building, training, and evaluating our first CNN
- Dataset description
- Step 1 – Loading the required packages
- Step 2 – Loading the training/test images to generate train/test set
- Step 3- Defining CNN hyperparameters
- Step 4 – Constructing the CNN layers
- Step 5 – Preparing the TensorFlow graph
- Step 6 – Creating a CNN model
- Step 7 – Running the TensorFlow graph to train the CNN model
- Step 8 – Model evaluation
- Dataset description
- Model performance optimization
- Summary
- Popular CNN Model Architectures
- Transfer Learning
- Autoencoders for CNN
- Object Detection and Instance Segmentation with CNN
- The differences between object detection and image classification
- Traditional, nonCNN approaches to object detection
- R-CNN – Regions with CNN features
- Fast R-CNN – fast region-based CNN
- Faster R-CNN – faster region proposal network-based CNN
- Mask R-CNN – Instance segmentation with CNN
- Instance segmentation in code
- References
- Summary
- GAN: Generating New Images with CNN
- Attention Mechanism for CNN and Visual Models
- Other Books You May Enjoy
Feature matching
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