This is a repository that contains an implementation of Wassertein-GAN (Generative Adversarial Network) using the PyTorch framework for generating batik patterns.
This project aims to generate new batik patterns using generative techniques by leveraging Wassertein-GAN. Wassertein-GAN is a variation of the GAN architecture that has advantages in addressing mode collapse issues and producing more stable results.
In this project, we use a dataset containing various examples of batik patterns that have been collected. We implement Wassertein-GAN using PyTorch, a popular and powerful deep learning framework. We train the GAN model to learn the distribution of the training batik data and then generate new batik samples with a similar style.
Implementation of Wassertein-GAN using PyTorch Training the GAN model on a batik pattern dataset Generating new batik patterns based on the trained model Simple user interface for running the batik pattern generation