An implementation of CycleGan using TensorFlow

Tensorflow implementation for learning an image-to-image translation without input-output pairs. https://arxiv.org/pdf/1703.10593.pdf

Reimplementation of CycleGAN

Tensorflow implementation of CycleGAN

An end-to-end signature verification system to extract, clean and verify signatures in documents. Signatures are detected using YOLOv5. Noise is cleaned using a CycleGAN approach and verified. Keras / Tensorflow / PyTorch

Keras implementation of CycleGAN using a tensorflow backend.

Deep Learning Summer School + Tensorflow + OpenCV cascade training + YOLO + COCO + CycleGAN + AWS EC2 Setup + AWS IoT Project + AWS SageMaker + AWS API Gateway + Raspberry Pi3 Ubuntu Core

Fog Simulation using Generative Adversarial Networks (GAN). This code is the implementation of the master thesis Simulating Weather Conditions on Digital Images. It uses a modified CycleGAN model to synthesize fog on clear images.

Implementation of GAN architectures for Voice Conversion

TensorFlow Distributed Image Serving - A lightweight, RESTful remote inference library for decoupling deep learning development and deployment. Example usage cases with CycleGAN and Faster R-CNN. Includes tutorial ipynbs.

Lightweight CycleGAN tensorflow implementation 🦁 <-> 🐆

A Tensorflow implementation of Cycle-Consistent Adversarial Networks.

Patch-based 3D Cycle-GAN for volumetric medical image synthesis

Tensorflow implementation of a CycleGAN with a 1D Convolutional Neural Network and Gated units with options for the residual connections, dilations and a PostNet.

Colorization of grayscale images using CycleGAN in TensorFlow.

A simple code of CycleGAN which is easy to read is implemented by TensorFlow

The GAN Book: Train stable Generative Adversarial Networks using TensorFlow2, Keras and Python.

tensorflow 2.0 implementation of CycleGAN, easy to use.

Simple Tensorflow implementation of CycleGAN

Nonparallel Emotional Speech Conversion with MUNIT. Introduction: This is a tensorflow implementation of paper(https://arxiv.org/pdf/1811.01174.pdf) Nonparallel Emotional Speech Conversion. It is an end-to-end voice conversion system which can change the speaker's emotion. For example, neutral to angry, sad to happy. The model aims at generating speech with desired emotions while keeping the original linguistic content and speaker identity. It first extracts acoustic features from raw audio, then learn the mapping from source emotion to target emotion in the feature space, and finally put those features together to rebuild the waveform. In our approach, three types of features are considered: Features: Fundamental frequency (log F_0), converted by logarithm Gaussian normalized transformation Power envelope, converted by logarithm Gaussian normalized transformation Mel-cepstral coefficients (MCEPs), a representation of spectral envelope, trained by CycleGAN Aperiodicities (APs), directly used without modification. Dependencies: Python 3.5, Numpy 1.15, TensorFlow 1.8, LibROSA 0.6, FFmpeg 4.0, PyWorld

CycleGAN implemented in Tensorflow 2.0

Pix2Pix and CycleGAN generative adversarial networks (GANs) to convert thermal to visible images using TensorFlow

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TensorFlow 2 implementation of CycleGAN with multi-GPU training.

Neural style transfer implementation using Tensorflow and unpaired image-to-image translation using CycleGANs

A Tensorflow implementation of CycleGAN-VC2

CycleGAN for hyperspectral image classification in TensorFlow 2.*

Cycle-Consistent Adversarial Networks (CycleGAN) using Tensorflow 2.0

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Tensorflow implementation of CycleGAN VC