Generating retro pixel game characters with Generative Adversarial Networks. Dataset "TinyHero" included.
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Updated
May 24, 2020 - Jupyter Notebook
Generating retro pixel game characters with Generative Adversarial Networks. Dataset "TinyHero" included.
DCGAN model to generate metal surface defect images
The objective of this project is to use the following Kaggle dataset (https://www.kaggle.com/datasets/soumikrakshit/anime-faces), in order to generate anime faces using a Deep Convolutional Generative Adversarial Network (DCGAN).
Simple implement of DCGAN on CIFAR10 with one code
Generation Of Synthetic Images From Fashion MNIST Dataset With DCGANs In Keras.
Generating simpson faces using Deep Convolutional Generative Adversarial Networks, written in PyTorch.
Road towards diffusion models.
Repository of all notebooks used in the GANs and VAEs event.
Implementations of GANs
Generate Lego Minifigures & Faces implementing four different type of GANs in Pytorch
This is repository teaching PyTorch1.0.
Deep Convolutional Generative Adversarial Networks
A web-app based on Wasserstein Generative Adversarial Network architecture with GP that generates multiple realistic paintings, trained on 8k Albrecht Dürer's paintings, includes super-res mode.
PyTorch implementation of various GAN architectures.
DCGAN Fashion MNIST generator
PyTorch implementation of the conditional Deep Convolutional Generative Adversarial Networks (cDCGAN) for the Google's "Quick, Draw!" dataset.
Using Generative Adversarial Networks to generate bird photos.
Implemented basic deep learning models using PyTorch
Deep Convolutional Generative Adversarial Network (DCGAN) implementation using PyTorch trained on the MNIST dataset to generate images of handwritten digits
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