Naresh1318/Adversarial_Autoencoder
A wizard's guide to Adversarial Autoencoders
This is a tool for machine learning practitioners and researchers to experiment with different types of Adversarial Autoencoders (AAEs). It allows you to feed in image datasets, like handwritten digits, and explore how these models can learn efficient data representations, generate new images, and disentangle features like style and content. The primary output includes trained models and visualizations of the learned latent spaces and generated images.
433 stars.
Use this if you are a machine learning researcher or student who wants to understand, implement, and experiment with adversarial autoencoder architectures for image generation, representation learning, or semi-supervised classification.
Not ideal if you are a non-technical user looking for a ready-to-use application for image processing, or if you need to deploy a production-ready system without deep understanding of model training.
Stars
433
Forks
115
Language
Python
License
MIT
Last pushed
Mar 09, 2026
Commits (30d)
0
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