In the previous sections, we learned about the issue of translation in images and how a CNN works. In this section, we will leverage that knowledge to learn how a CNN works toward improving prediction accuracy when an image is translated.
CNNs to improve accuracy in the case of image translation
by V Kishore Ayyadevara
Neural Networks with Keras Cookbook
- Title Page
- Copyright and Credits
- Dedication
- About Packt
- Contributors
- Preface
- Building a Feedforward Neural Network
- Building a Deep Feedforward Neural Network
- Training a vanilla neural network
- Scaling the input dataset
- Impact on training when the majority of inputs are greater than zero
- Impact of batch size on model accuracy
- Building a deep neural network to improve network accuracy
- Varying the learning rate to improve network accuracy
- Varying the loss optimizer to improve network accuracy
- Understanding the scenario of overfitting
- Speeding up the training process using batch normalization
- Applications of Deep Feedforward Neural Networks
- Building a Deep Convolutional Neural Network
- Transfer Learning
- Gender classification of the person in an image using CNNs
- Gender classification of the person in image using the VGG16 architecture-based model
- Visualizing the output of the intermediate layers of a neural network
- Gender classification of the person in image using the VGG19 architecture-based model
- Gender classification using the Inception v3 architecture-based model
- Gender classification of the person in image using the ResNet 50 architecture-based model
- Detecting the key points within image of a face
- Detecting and Localizing Objects in Images
- Introduction
- Creating the dataset for a bounding box
- Generating region proposals within an image, using selective search
- Calculating an intersection over a union between two images
- Detecting objects, using region proposal-based CNN
- Performing non-max suppression
- Detecting a person using an anchor box-based algorithm
- Image Analysis Applications in Self-Driving Cars
- Image Generation
- Introduction
- Generating images that can fool a neural network using adversarial attack
- DeepDream algorithm to generate images
- Neural style transfer between images
- Generating images of digits using Generative Adversarial Networks
- Generating images using a Deep Convolutional GAN
- Face generation using a Deep Convolutional GAN
- Face transition from one to another
- Performing vector arithmetic on generated images
- Encoding Inputs
- Text Analysis Using Word Vectors
- Introduction
- Building a word vector from scratch in Python
- Building a word vector using the skip-gram and CBOW models
- Performing vector arithmetic using pre-trained word vectors
- Creating a document vector
- Building word vectors using fastText
- Building word vectors using GloVe
- Building sentiment classification using word vectors
- Building a Recurrent Neural Network
- Applications of a Many-to-One Architecture RNN
- Sequence-to-Sequence Learning
- End-to-End Learning
- Audio Analysis
- Reinforcement Learning
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CNNs to improve accuracy in the case of image translation
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