Keras GAN | Awesome Repository

Keras-GAN is a collection of generative adversarial network implementations built with Keras for synthetic data generation and image manipulation. It provides frameworks for image-to-image translation, image inpainting, and neural image super-resolution.

The library includes tools for learning disentangled latent space representations to control specific attributes of synthetic outputs. It also features capabilities for image domain translation using paired or unpaired data and the ability to fill corrupted or missing image parts by analyzing surrounding visual context.

The project covers generative tasks such as increasing image resolution for high-definition output and adapting data between different domains to improve classifier accuracy.

Features

Generative Adversarial Networks - Implements a comprehensive collection of generative adversarial network architectures for synthetic data and image generation.
Adversarial Loss Functions - Uses adversarial loss functions to optimize the generator by evaluating generated data against a discriminator.
Convolutional Feature Extractors - Utilizes convolutional feature extractors to learn spatial hierarchies and local patterns within image data.
Encoder-Decoder Architectures - Employs encoder-decoder architectures to compress images into latent vectors for noise filtering and feature extraction.

Features

Generative Adversarial Networks - Implements a comprehensive collection of generative adversarial network architectures for synthetic data and image generation.
Adversarial Loss Functions - Uses adversarial loss functions to optimize the generator by evaluating generated data against a discriminator.
Convolutional Feature Extractors - Utilizes convolutional feature extractors to learn spatial hierarchies and local patterns within image data.
Encoder-Decoder Architectures - Employs encoder-decoder architectures to compress images into latent vectors for noise filtering and feature extraction.

The project covers generative tasks such as increasing image resolution for high-definition output and adapting data between different domains to improve classifier accuracy.