In this project we created a Generative adversal neural network that can generate hand written numbers based on the Minst data set.
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Updated
Feb 28, 2021 - Jupyter Notebook
In this project we created a Generative adversal neural network that can generate hand written numbers based on the Minst data set.
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 .
An attempt to learn how to build discriminator networks for GAN
This code implements an example of a CGAN deep learning model using PyTorch. The architecture used for the generator and discriminator is MLP (multi layer perceptron) network. This model is trained with MNIST dataset and finally it can generate images of numbers 0 to 9 according to the label we specify for it.
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.
Tensorflow implementation of Conditional GAN trained on MNIST dataset
Generate fake faces using generative adversarial network
self-supervised learning to generate new anime faces using GANS
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.
Underwater pipe image enhancement through a pix-to-pix GAN model and a series of statistical OpenCV filters
In this project, we worked on generating realistic looking human faces using Generative Adversarial Networks.
Multi-tenant App using Spring boot & hibernate.
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
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