eriklindernoren/Keras-GAN
Keras GAN
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.
- Generative Latent Mappings - Maps random noise vectors to structured representations to synthesize new synthetic data samples.
- Image-to-Image Translation - Transforms images from one style or content domain to another using generative translation techniques.
- Image Super Resolution Models - Increases image resolution to create high-definition versions through photo-realistic neural reconstruction.
- Keras GAN Implementations - Provides a comprehensive library of generative adversarial networks built with the Keras framework.
- Image Inpainting - Fills corrupted or missing parts of images by analyzing surrounding visual context through deep learning.
- Image Translation Frameworks - Offers a framework for translating images between domains and styles using paired and unpaired data.
- Disentangled - Extracts structured and disentangled representations to allow precise control over specific attributes of synthetic outputs.
- Latent Space Generative Models - Implements architectures that manipulate compressed latent representations to control synthetic output attributes.
- Skip-Connection Architectures - Implements skip-connections to preserve high-resolution spatial information during image-to-image translation.
- Synthetic Data Generators - Creates new synthetic data samples from learned distributions using adversarial network architectures.
- Interpretable Representation Analysis - Extracts structured and disentangled latent representations to control specific attributes of generated outputs.
- Domain Adaptation Techniques - Provides domain adaptation techniques to translate data between distributions and improve classifier accuracy.
- Computer Vision Libraries - Keras implementations of various GANs.
- Computer Vision Models - Implementations of CycleGAN, DualGAN, and super-resolution models.
- Generative Model Implementations - Extensive library of GAN implementations using Keras.
