A template repository for GANs

Introduction to generative adversial network

Code for the text point cloud group assignment (part of the Deep Neural Engineering AI course)

A Conditional Deep Convolutional Generative Adversarial Network implemented in PyTorch, trained on the Fashion MNIST dataset.

Analysis of Skin Lesion Images to segment lesion regions and classify lesion type using adversarial deep learning.

[NeurIPS 2022] Efficient Spatially Sparse Inference for Conditional GANs and Diffusion Models

Conditional DCGAN

PyTorch implementation of Conditional Generative Adversarial Networks (cGAN) for image colorization of the MS COCO dataset

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.

Resolving semantic confusions for improved zero-shot detection (BMVC 2022)

This repo contains code for enhancing a degraded image using CGAN

Implementation of different GANs using TensorFlow

In-depth tutorials on deep learning. The first one is about image colorization using GANs (Generative Adversarial Nets).

Logistic regression, deep learning, YOLO, Recursive Neural Networks, GAN and Conditional GAN

Implementation of different types of GANs in TensorFlow and Pyrorch

Advanced Topics in Machine Learning - Autumn Semester 2023 - Indian Institute of Technology Bombay

Estimating brain activity for a stimulus as measured by fMRI using a volumetric conditional Generative Adversarial Network (GAN) model.

Companion repository to GANs in Action: Deep learning with Generative Adversarial Networks