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.
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What if we replace the Convolution Layers inside GANs (Generative Adversarial Networks) with Cellular Automata. And train the Cellular Automata using a Genetic Algorithm.
A decentralized supply chain solution that integrates blockchain smart contracts, predictive analytics, and a modern web interface. Leveraging Ethereum (via Hardhat) for secure and transparent contract execution, combined with a Python-based predictive modeling engine and a responsive front-end
Website of the "Towards Practical Single-shot Motion Synthesis" paper.
PixelPerfect delivers fast and powerful image enhancements. Whether it's generating captions or summaries or sharpening images
A playground to generate images from any text prompt using Stable Diffusion (past: using DALL-E Mini)
국립공원공단 치악산 대국민 탐방서비스
AnimeGAN.js: Photo Animation for Everyone
Owner :: Laurent Poyet - Updating https://github.com/carpedm20/DCGAN-tensorflow TF and Python modules
This platform aims to connect painters to client. Painter can share/sell their paintings whereas for the clients they can buy painting an enrich their profile with modern paintings. The platform also offers AI services (GAN) to generate live images on sport based on prompt text
YC Hackathon 2018 Winner Project. BEN: A decentralized chatbot that uses federated learning.
AnimeGAN.js: Photo Animation for Everyone
Create Anime Characters with MakeGirlsMoe
P-O-G [Project Of Generative model] - 인공지능 모델 구매 및 판매 플랫폼 (ML서버 통해 스마트폰으로 모델 이용 가능)
A backend server for assisted generation of synthetic fingerprints with the help of pre-trained GAN models 🖐
Robust way to explore GAN model latent space on web using three.js & t-SNE
Inofficial implementation of the paper "SinGAN: Learning a Generative Model from a Single Natural Image"
Released June 10, 2014