A PyTorch implementation using CycleGAN architecture, to read in an image from a set X and transform it so that it looks as if it belongs in set Y .
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Updated
Feb 21, 2021 - Jupyter Notebook
A PyTorch implementation using CycleGAN architecture, to read in an image from a set X and transform it so that it looks as if it belongs in set Y .
Tensorflow implementation of Conditional GAN trained on MNIST dataset
An attempt to learn how to build discriminator networks for GAN
Trained a generative Adverserial Network (GAN) which when given the satellite image of a place as input, outputs the Map image of that same location. It was trained using standard adverserial training.
Deep Convolutional Generative Adversarial Network for Street View House Numbers dataset.
The @encapsule/arccore package contains runtime algorithms for schematizing, filtering, routing, and modeling strongly-typed in-memory data with mathematical graphs and JSON-serializable data types for use in Node.js and HTML5 application services implemented in JavaScript.
(experimenting) A discriminator component for MultiView-GAN
Probabilistic Future Video Frame Prediction using Generative Adversarial Networks by employing a regret minimization strategy for training GANs.
We will visualize the style transfer output produced by monet_generator_model. We take 5 sample images that are photos of beautiful landscapes in the original dataset and feed them to the model.
Generate Anime Style Face Using DCGAN and Explore Its Latent Feature Representation
Using Unconditional GANs to produce new football jersey ideas
A pytorch implementation of GAN
Project for Deep Learning Nanodegree, unit 5 (Generative Adversarial Networks).
Create Synthetic-Images in TensorFlow using Generative Adversarial Network and Deepfakes using Keras.
In this project, we worked on generating realistic looking human faces using Generative Adversarial Networks.
A GAN that sythesizes new faces alike faces from celebA dataset
A PyTorch Implementation of DCGAN on a dataset of celebrity faces.
Implementation of the paper "Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data." 🖼️
A python package that'll help you train DCGAN models with your own image based data.
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