DCGAN Projects Repository implemented using Keras. (Includes pre-trained model)

Generation Of Synthetic Images From Fashion MNIST Dataset With DCGANs In Keras.

Coursera hand held project to understand the deepfakes using keras (DCGAN)

This 'Generative Adversarial Network' project was implemented in grad course CSE-676 : Deep Learning [Fall 2019 @UB_SUNY] Course Instructor : Sargur N. Srihari(https://cedar.buffalo.edu/~srihari/)

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.

Implementation of DCGAN model to train a neural network on mnist dataset and generate fake handwritten digits close enough to the real images from the dataset.

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

Keras implementation of dcgan, wgan and wgan-gp with digit-MNIST dataset for tutorials.

Using DCGAN to generate abstract art

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.

deep convolutional generative adversarial network for FashionMNIST dataset with Keras and keras-adversarial

DCGAN to generate Anime Character's Faces

Conditional face generation experiments using GAN models on CelebA dataset.

Membuat Karakter Anime Menggunakan Deep Convolutional Generative Adversarial Networks (DCGANs) dan Keras

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

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

Training a DCGAN to generate new images of faces that look realistic as possible.

Generate Anime Style Face Using DCGAN and Explore Its Latent Feature Representation