Displaying 1 to 11 from 11 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.

faceswap-GAN - A denoising autoencoder + adversarial losses and attention mechanisms for face swapping

  •    Jupyter

Adding Adversarial loss and perceptual loss (VGGface) to deepfakes'(reddit user) auto-encoder architecture. Here is a playground notebook for faceswap-GAN v2.2 on Google Colab. Users can train their own model in the browser without GPU required.

BicycleGAN - [NIPS 2017] Toward Multimodal Image-to-Image Translation

  •    Python

Pytorch implementation for multimodal image-to-image translation. For example, given the same night image, our model is able to synthesize possible day images with different types of lighting, sky and clouds. The training requires paired data. Note: The current software works well with PyTorch 0.4. Check out the older branch that supports PyTorch 0.1-0.3.




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.

asrgen - Attacking Speaker Recognition with Deep Generative Models

  •    Jupyter

PyTorch implementation of Attacking Speaker Recognition Systems with Deep Generative Models. This implementation uses code from the following repos: [NVIDIA's Tacotron 2] (https://github.com/nvidia/tacotron2), Martin Arjovsky and Prem Seetharaman.

FaceData - A macOS app to parse face landmarks from a video for GANs training

  •    Swift

A macOS application used to auto-annotate landmarks from a video. Those landmarks can further be used as training data for Generative Adversarial Networks (GANs). You can either download the binary file from Rease or build the source code using Xcode.

model-zoo - Implementations of various Deep Learning models in PyTorch and TensorFlow.

  •    Python

This repository contains implementations of various deep learning research papers. The models are broadly categorised into the folders Generative (e.g. various generative models), NLP (e.g. various recurrent neural networks (RNNs) and natural language processing (NLP) models), Classification (e.g. various CNN models to classify images), Object Detection, Multimodal , Super resolution , 3D Computer Vision. See the READMEs of respective models for more information.


Generative-Adversarial-Bots - We compare the performance and human-likeness of conversational chatbots by generating conversation between two bots, and evaluating the response using Turing Tests

  •    Jupyter

This project is looking for contributors. If you are interested, please start an issue and tag me (@xhlulu) in. Briefly, Generative Adversarial Bots (GABs) are bots that are pitched up against each other, and generate a conversation that is used to train a third bot.

Cartoonizer-with-TFLite - How to create a Cartoonizer with TensorFlow Lite models.

  •    Jupyter

This is the GitHub repository for an end-to-end tutorial on How to Create a Cartoonizer with TensorFlow Lite, published on the official TensorFlow blog. The tutorial demonstrates the steps for TFLite model saving, conversion and all the way up to model deployment on an Android App. It's one of a series of the End-to-End TensorFlow Lite Tutorials. See the full list of TensorFlow Lite samples and learning resources on awesome-tflite. In this project repo, the ml folder contains the model files, and the instructions on how to save the model, and convert it to selfe2anime.tflite, and add metadata to it via either command line or a Colab notebook.

Selfie2Anime-with-TFLite - How to create Selfie2Anime from tflite model to Android.

  •    Jupyter

Selfie2Anime with TensorFlow Lite is one of the many End-to-End TensorFlow Lite Tutorials. See the full list of TensorFlow Lite samples and learning resources on awesome-tflite. The ml folder contains the model files, and the instructions on how to save the model, and convert it to selfe2anime.tflite, and add metadata to it via either command line or a Colab notebook.






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