MNIST-DCGAN is a deep learning project that uses a DCGAN to generate realistic handwritten digits from the MNIST dataset. It demonstrates how a generator and discriminator network compete to create and evaluate images, improving the generator’s output over time.

A repository showing the knowledge in some of the machine learning technologies there are.

In this project, I aim to develop a robust system to classify real and synthetic retina images using advanced machine learning. I will generate synthetic retina images from a single eye photo using Deep Convolutional Generative Adversarial Networks (DCGAN) and classify them with a Convolutional Neural Network (CNN).

A Deep Convolutional Generative Adversarial Network (DCGAN) is an extension of the standard GAN architecture that uses deep convolutional networks for both the generator and discriminator models.

A PhotoReaslistic AI GAN model to generate photorealistic faces on a large scale

Generating high-resolution adversarial images with GAN

The combined method between applying the CNNs to GANs models is called Deep Convolutional Generative Adversarial Networks (DCGANs).

The Generative Adversarial Networks with Python would serve as our primary reference throughout the project. The models would be trained on the MNIST dataset. The official TensorFlow framework and documentation will be used to implement the different architectures on Python. These papers would be used to implement various evaluation met

Implementation of different types of GANs in TensorFlow and Pyrorch

In this repository, I will implement variety of GAN model using Tensorflow framework

Fake Face Generation using DCGAN

This project utilizes the DCGAN architecture to generate lifelike human faces. The generator is trained to produce new images, while the discriminator is trained to differentiate between real and generated images. As the model undergoes further training, it progressively improves its ability to generate more realistic results.

Create images of Pokemon using a Deep Convolutional Generative Adversarial Network.

Keras model subclassing implementation of a GAN. Based on https://twitter.com/fchollet/status/1250622989541838848

🙄 Difficult algorithm, Simple code.

DCGAN to generate cat pictures

In this script, we use Deep Convolutional Generative Adversarial Networks (DCGANs) to generate new images that resemble CIFAR10 dataset images.

Damaged image restauration using a GAN model with a U-Net with skip connection autoencoder as a generator

This project is a semi-supervised approach to detect emotions on faces in-the-wild using GAN

A general purpose DCGAN that generates 256 x 256 RGB images. Tested with the 11K hands Dataset