Displaying 1 to 14 from 14 results

CycleGAN - Software that can generate photos from paintings, turn horses into zebras, perform style transfer, and more

  •    Lua

This package includes CycleGAN, pix2pix, as well as other methods like BiGAN/ALI and Apple's paper S+U learning. The code was written by Jun-Yan Zhu and Taesung Park. Note: Please check out PyTorch implementation for CycleGAN and pix2pix. The PyTorch version is under active development and can produce results comparable or better than this Torch version.

pytorch-CycleGAN-and-pix2pix - Image-to-image translation in PyTorch (e

  •    Python

This is our PyTorch implementation for both unpaired and paired image-to-image translation. It is still under active development. The code was written by Jun-Yan Zhu and Taesung Park, and supported by Tongzhou Wang.

PaddleGAN - 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

  •    Python

PaddleGAN provides developers with high-performance implementation of classic and SOTA Generative Adversarial Networks, and supports developers to quickly build, train and deploy GANs for academic, entertainment and industrial usage. GAN-Generative Adversarial Network, was praised by "the Father of Convolutional Networks" Yann LeCun (Yang Likun) as [One of the most interesting ideas in the field of computer science in the past decade]. It's the one research area in deep learning that AI researchers are most concerned about.

Hands-On-Deep-Learning-Algorithms-with-Python - Master Deep Learning Algorithms with Extensive Math by Implementing them using TensorFlow

  •    Jupyter

Deep learning is one of the most popular domains in the artificial intelligence (AI) space, which allows you to develop multi-layered models of varying complexities. This book is designed to help you grasp things, from basic deep learning algorithms to the more advanced algorithms. The book is designed in a way that first you will understand the algorithm intuitively, once you have a basic understanding of the algorithms, then you will master the underlying math behind them effortlessly and then you will learn how to implement them using TensorFlow step by step. The book covers almost all the state of the art deep learning algorithms. First, you will get a good understanding of the fundamentals of neural networks and several variants of gradient descent algorithms. Later, you will explore RNN, Bidirectional RNN, LSTM, GRU, seq2seq, CNN, capsule nets and more. Then, you will master GAN and various types of GANs and several different autoencoders.

tensorflow-cyclegan - Lightweight CycleGAN tensorflow implementation 🦁 <-> 🐆

  •    Python

A lightweight CycleGAN tensorflow implementation. If you plan to use a CycleGAN model for real-world purposes, you should use the Torch CycleGAN implementation.

pytorch_cycle_gan - CycleGAN with Productive Generate APIs

  •    Python

this repo based on the original implementation of CycleGAN: https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix.git, in this version I reconstruct some code and made a generate API to simply generate image from your own single image and your trained model. I only trained about 50 epochs, but the result is fair enough for now. Laterly I will finish horse2zebra model, and update some more results.

CycleGAN-Tensorflow-PyTorch - CycleGAN Tensorflow PyTorch

  •    Python

2018.04.13: We modify the codes: use the newest tensorflow 1.7 API, and remove the redundancies. 2017.12.22: We add a simple PyTorch implementation, see the "pytorch" folder.

pixel-styler - Concise implementation of image-to-image translation.

  •    Python

This is a concise refactoring version of official PyTorch implementation for image-to-image translation. If you would like to apply a pre-trained model to a collection of input photos (without image pairs), please use --dataset_mode single and --model test options. Here's command to apply a model to Facade label maps (stored in the directory facades/testB).

SNE-RoadSeg2 - 🌱 SNE-RoadSeg in PyTorch, ECCV 2020 by Rui (Ranger) Fan & Hengli Wang, but now we have improved it

  •    Python

This SNE-RoadSeg2 is based on the official pytorch implementation of SNE-RoadSeg: Incorporating Surface Normal Information into Semantic Segmentation for Accurate Freespace Detection, accepted by ECCV 2020. This is their project page. In this repo, we provide the training and testing setup for the KITTI Road Dataset. We test our code in Python 3.7, CUDA 10.0, cuDNN 7 and PyTorch 1.1. We provide Dockerfile to build the docker image we use.

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