raahii/infogan-pytorch
:art: A PyTorch implementation of InfoGAN.
This project helps machine learning researchers or practitioners explore how different aspects of an image, like a digit's type, rotation, or thickness, are represented in a generated image. You provide a dataset of images and define latent variables, then it generates new images while letting you control specific, interpretable features. It's for anyone experimenting with generative models and seeking to understand the underlying factors of variation in image data.
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Use this if you are a machine learning researcher or student who wants to generate images and understand which hidden features (like digit type or rotation) are responsible for specific visual characteristics in the generated output.
Not ideal if you are looking for an out-of-the-box solution to generate highly realistic, complex images for production use, or if you don't have a background in machine learning models and training.
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Mar 25, 2021
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