deep convolutional generative adversarial network for FashionMNIST dataset with Keras and keras-adversarial
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Updated
Jun 1, 2019 - Jupyter Notebook
deep convolutional generative adversarial network for FashionMNIST dataset with Keras and keras-adversarial
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/)
Coursera hand held project to understand the deepfakes using keras (DCGAN)
DCGAN Projects Repository implemented using Keras. (Includes pre-trained model)
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.
Generation Of Synthetic Images From Fashion MNIST Dataset With DCGANs In Keras.
Keras implementation of dcgan, wgan and wgan-gp with digit-MNIST dataset for tutorials.
Training a DCGAN to generate new images of faces that look realistic as possible.
DCGAN to generate Anime Character's Faces
Generate Anime Style Face Using DCGAN and Explore Its Latent Feature Representation
DCGAN to generate Anime Character's Faces
Conditional face generation experiments using GAN models on CelebA dataset.
Using DCGAN to generate abstract art
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.
The combined method between applying the CNNs to GANs models is called Deep Convolutional Generative Adversarial Networks (DCGANs).
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.
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.
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