Image-to-Image Translation in PyTorch
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Updated
Aug 31, 2021 - Python
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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.
Image-to-Image Translation in PyTorch
A list of all named GANs!
Software that can generate photos from paintings, turn horses into zebras, perform style transfer, and more.
A MNIST-like fashion product database. Benchmark
PyTorch tutorials and fun projects including neural talk, neural style, poem writing, anime generation (《深度学习框架PyTorch:入门与实战》)
Image-to-image translation with conditional adversarial nets
Keras implementations of Generative Adversarial Networks.
A tensorflow implementation of "Deep Convolutional Generative Adversarial Networks"
Deep Learning and Reinforcement Learning Library for Scientists and Engineers
Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.
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Awesome paper list with code about generative adversarial nets
深度学习入门教程, 优秀文章, Deep Learning Tutorial
Synthesizing and manipulating 2048x1024 images with conditional GANs
Tensorflow tutorial from basic to hard, 莫烦Python 中文AI教学
PaddlePaddle GAN library, including lots of interesting applications like First-Order motion transfer, wav2lip, picture repair, image editing, photo2cartoon, image style transfer, and so on.
Washington University (in St. Louis) Course T81-558: Applications of Deep Neural Networks
Interactive Image Generation via Generative Adversarial Networks
Collection of generative models in Tensorflow
This repo contains the source code in my personal column (https://zhuanlan.zhihu.com/zhaoyeyu), implemented using Python 3.6. Including Natural Language Processing and Computer Vision projects, such as text generation, machine translation, deep convolution GAN and other actual combat code.
[CVPR2020] Adversarial Latent Autoencoders
Create Anime Characters with MakeGirlsMoe
A denoising autoencoder + adversarial losses and attention mechanisms for face swapping.
Official pytorch implementation of the paper: "SinGAN: Learning a Generative Model from a Single Natural Image"
人像卡通化探索项目 (photo-to-cartoon translation project)
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Summaries of machine learning papers
Multimodal Unsupervised Image-to-Image Translation
There are many links in Kinetics that have expired. As as result, everyone might not be using the same Kinetics dataset. As a reference, the statistics of the Kinetics dataset used in PySlowFast can be found here, https://github.com/facebookresearch/video-nonlocal-net/blob/master/DATASET.md. However, I cannot seem to find similar information for gluoncv. Will you guys be sharing the statistics and