Generative adversarial networks (GAN) are a class of generative machine learning frameworks. A GAN consists of two competing neural networks, often termed the Discriminator network and the Generator network. GANs have been shown to be powerful generative models and are able to successfully generate new data given a large enough training dataset.
Imaginify is a cutting-edge AI-powered SaaS platform that transforms images using advanced machine learning algorithms. Built with modern web technologies, it provides users with powerful image editing capabilities through an intuitive and beautiful interface.
The first of a kind "AGENT AS A BACKEND" : Advanced AI/ML Agent Framework with TypeScript support, multi-model capabilities, vision controls, and comprehensive AI/ML model support including LLM, SLM, MLM, Vision, ASR, TTS, RL, GNN, RNN, CNN, GAN, Diffusion, Transformer, MLP, Autoencoder, BERT, RAG, Hybrid, Foundation, and fine-tuning
Reincarnation of the free age progression filter app
This is a mono-repo hosting the source code of a GAN Neural Network model capable of producting (and being trained on) 64x64 images (1 or 3 channels), along with a helper/wrapper library that can be used in any javascript context to load a pre-trained model for immediate use.
Terrain generation tool, using real-earth data and deep learning techniques
A simple React web app to showcase the power of GFP-GAN.
Fast and simple to use neural network implementation in pure TypeScript with GPU support!
A Deep learning project which targets improving the camera quality for budget phones, the Fixel app provides the user capability to capture images through the Fixel App made of Ionic and sent captured image to web API. The Fixel Web Api Is bundled with ProSR GAN for converting low-resolution images to high-resolution images.
Save Princess Attention From 8 Discriminators
App for evaluating images produced by GAN. Just choose which one is real
TobaHackathon 2021 のチーム Gelpus の作品のレポジトリです。
GAN Playground - Experiment with Generative Adversarial Nets in your browser. An introduction to GANs.
Released June 10, 2014