HyperGAN - A composable Generative Adversarial Network(GAN) with API and command line tool.

  •        557

A composable GAN API and CLI. Built for developers, researchers, and artists. HyperGAN is currently in open beta.

https://github.com/255BITS/HyperGAN

Tags
Implementation
License
Platform

   




Related Projects

gan-playground - GAN Playground - Experiment with Generative Adversarial Nets in your browser

  •    TypeScript

GAN Playground lets you play around with Generative Adversarial Networks right in your browser. Currently, it contains three built-in datasets: MNIST, Fashion MNIST, and CIFAR-10. GAN Playground provides you the ability to set your models' hyperparameters and build up your discriminator and generator layer-by-layer. You can observe the network learn in real time as the generator produces more and more realistic images, or more likely, gets stuck in failure modes such as mode collapse.

All-About-the-GAN - All About the GANs(Generative Adversarial Networks) - Summarized lists for GAN

  •    Python

The purpose of this repository is providing the curated list of the state-of-the-art works on the field of Generative Adversarial Networks since their introduction in 2014. You can also check out the same data in a tabular format with functionality to filter by year or do a quick search by title here.

Generative-Adversarial-Networks - Tutorial on GANs

  •    Jupyter

My blog post on GANs and overview of some associated papers. Generative adversarial networks (GANs) are one of the hottest topics in deep learning. From a high level, GANs are composed of two components, a generator and a discriminator. The discriminator has the task of determining whether a given image looks natural (ie, is an image from the dataset) or looks like it has been artificially created. The task of the generator is to create natural looking images that are similar to the original data distribution, images that look natural enough to fool the discriminator network.

context-encoder - [CVPR 2016] Unsupervised Feature Learning by Image Inpainting using GANs

  •    Lua

If you could successfully run the above demo, run following steps to train your own context encoder model for image inpainting. Features for context encoder trained with reconstruction loss.

DiscoGAN-pytorch - PyTorch implementation of "Learning to Discover Cross-Domain Relations with Generative Adversarial Networks"

  •    Jupyter

PyTorch implementation of Learning to Discover Cross-Domain Relations with Generative Adversarial Networks. * All samples in README.md are genearted by neural network except the first image for each row. * Network structure is slightly diffferent (here) from the author's code.


ganlab - GAN Lab: An Interactive, Visual Experimentation Tool for Generative Adversarial Networks

  •    Javascript

GAN Lab is a novel interactive visualization tool for anyone to learn and experiment with Generative Adversarial Networks (GANs), a popular class of complex deep learning models. With GAN Lab, you can interactively train GAN models for 2D data distributions and visualize their inner-workings, similar to TensorFlow Playground. GAN Lab uses TensorFlow.js, an in-browser GPU-accelerated deep learning library. Everything, from model training to visualization, is implemented with JavaScript. Users only need a web browser like Chrome to run GAN Lab. Our implementation approach significantly broadens people's access to interactive tools for deep learning.

Hands-On-Meta-Learning-With-Python - Learning to Learn using One-Shot Learning, MAML, Reptile, Meta-SGD and more with Tensorflow

  •    Jupyter

Meta learning is an exciting research trend in machine learning, which enables a model to understand the learning process. Unlike other ML paradigms, with meta learning you can learn from small datasets faster. Hands-On Meta Learning with Python starts by explaining the fundamentals of meta learning and helps you understand the concept of learning to learn. You will delve into various one-shot learning algorithms, like siamese, prototypical, relation and memory-augmented networks by implementing them in TensorFlow and Keras. As you make your way through the book, you will dive into state-of-the-art meta learning algorithms such as MAML, Reptile, and CAML. You will then explore how to learn quickly with Meta-SGD and discover how you can perform unsupervised learning using meta learning with CACTUs. In the concluding chapters, you will work through recent trends in meta learning such as adversarial meta learning, task agnostic meta learning, and meta imitation learning.

generative-models - Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.

  •    Python

Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow. Also present here are RBM and Helmholtz Machine. Generated samples will be stored in GAN/{gan_model}/out (or VAE/{vae_model}/out, etc) directory during training.

simulated-unsupervised-tensorflow - TensorFlow implementation of "Learning from Simulated and Unsupervised Images through Adversarial Training"

  •    Python

TensorFlow implementation of Learning from Simulated and Unsupervised Images through Adversarial Training. Result of lambda=1.0 with optimizer=sgd after 8,000 steps.

Tensorflow-Tutorial - Tensorflow tutorial from basic to hard

  •    Python

In these tutorials, we will build our first Neural Network and try to build some advanced Neural Network architectures developed recent years. All methods mentioned below have their video and text tutorial in Chinese. Visit 莫烦 Python for more.

Machine-Learning / Deep-Learning / AI + Web3 -Tutorials

  •    Python

A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Climate / Energy, Automotives, Retail, Pharma, Medicine, Healthcare, Policy, Ethics and more.

practical-machine-learning-with-python - Master the essential skills needed to recognize and solve complex real-world problems with Machine Learning and Deep Learning by leveraging the highly popular Python Machine Learning Eco-system

  •    Jupyter

"Data is the new oil" is a saying which you must have heard by now along with the huge interest building up around Big Data and Machine Learning in the recent past along with Artificial Intelligence and Deep Learning. Besides this, data scientists have been termed as having "The sexiest job in the 21st Century" which makes it all the more worthwhile to build up some valuable expertise in these areas. Getting started with machine learning in the real world can be overwhelming with the vast amount of resources out there on the web. "Practical Machine Learning with Python" follows a structured and comprehensive three-tiered approach packed with concepts, methodologies, hands-on examples, and code. This book is packed with over 500 pages of useful information which helps its readers master the essential skills needed to recognize and solve complex problems with Machine Learning and Deep Learning by following a data-driven mindset. By using real-world case studies that leverage the popular Python Machine Learning ecosystem, this book is your perfect companion for learning the art and science of Machine Learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute Machine Learning systems and projects successfully.

tensorlayer - Deep Learning and Reinforcement Learning Library for Developers and Scientists

  •    Python

TensorLayer is a novel TensorFlow-based deep learning and reinforcement learning library designed for researchers and engineers. It provides a large collection of customizable neural layers / functions that are key to build real-world AI applications. TensorLayer is awarded the 2017 Best Open Source Software by the ACM Multimedia Society. Simplicity : TensorLayer lifts the low-level dataflow interface of TensorFlow to high-level layers / models. It is very easy to learn through the rich example codes contributed by a wide community.

qiskit-machine-learning - Quantum Machine Learning

  •    Python

The Machine Learning package simply contains sample datasets at present. It has some classification algorithms such as QSVM and VQC (Variational Quantum Classifier), where this data can be used for experiments, and there is also QGAN (Quantum Generative Adversarial Network) algorithm. We encourage installing Qiskit Machine Learning via the pip tool (a python package manager).

T2F - T2F: text to face generation using Deep Learning

  •    Python

Text-to-Face generation using Deep Learning. This project combines two of the recent architectures StackGAN and ProGAN for synthesizing faces from textual descriptions. The project uses Face2Text dataset which contains 400 facial images and textual captions for each of them. The data can be obtained by contacting either the RIVAL group or the authors of the aforementioned paper. The code is present in the implementation/ subdirectory. The implementation is done using the PyTorch framework. So, for running this code, please install PyTorch version 0.4.0 before continuing.

PyTorch-Tutorial - Build your neural network easy and fast

  •    Jupyter

In these tutorials for pyTorch, we will build our first Neural Network and try to build some advanced Neural Network architectures developed recent years. Thanks for liufuyang's notebook files which is a great contribution to this tutorial.

PyTorch-GAN - PyTorch implementations of Generative Adversarial Networks.

  •    Python

Collection of PyTorch implementations of Generative Adversarial Network varieties presented in research papers. Model architectures will not always mirror the ones proposed in the papers, but I have chosen to focus on getting the core ideas covered instead of getting every layer configuration right. Contributions and suggestions of GANs to implement are very welcomed. Synthesizing high resolution photorealistic images has been a long-standing challenge in machine learning. In this paper we introduce new methods for the improved training of generative adversarial networks (GANs) for image synthesis. We construct a variant of GANs employing label conditioning that results in 128x128 resolution image samples exhibiting global coherence. We expand on previous work for image quality assessment to provide two new analyses for assessing the discriminability and diversity of samples from class-conditional image synthesis models. These analyses demonstrate that high resolution samples provide class information not present in low resolution samples. Across 1000 ImageNet classes, 128x128 samples are more than twice as discriminable as artificially resized 32x32 samples. In addition, 84.7% of the classes have samples exhibiting diversity comparable to real ImageNet data.

tflearn - Deep learning library featuring a higher-level API for TensorFlow.

  •    Python

TFlearn is a modular and transparent deep learning library built on top of Tensorflow. It was designed to provide a higher-level API to TensorFlow in order to facilitate and speed-up experimentations, while remaining fully transparent and compatible with it. The high-level API currently supports most of recent deep learning models, such as Convolutions, LSTM, BiRNN, BatchNorm, PReLU, Residual networks, Generative networks... In the future, TFLearn is also intended to stay up-to-date with latest deep learning techniques.






We have large collection of open source products. Follow the tags from Tag Cloud >>


Open source products are scattered around the web. Please provide information about the open source projects you own / you use. Add Projects.