AmirhosseinHonardoust/Handwritten-Digit-GAN
A PyTorch implementation of a Deep Convolutional GAN (DCGAN) trained on MNIST. Includes training scripts, generator & discriminator models, random sample generation, latent space interpolation, and loss curve visualization to create realistic handwritten digit images.
This project helps machine learning practitioners or researchers generate new, realistic handwritten digit images. You provide random noise, and the system produces diverse, human-like digits. It's designed for those exploring generative models and synthetic data creation.
No commits in the last 6 months.
Use this if you need to generate artificial handwritten digit images for research, dataset augmentation, or to understand how generative adversarial networks (GANs) work.
Not ideal if you need to recognize or classify existing handwritten digits, or if you're looking for an off-the-shelf solution for general image generation beyond simple digits.
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Language
Python
License
MIT
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Last pushed
Sep 12, 2025
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