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Toolbox for experimenting with (Overcomplete) Dictionary Learning for Vision model

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Overcomplete is a compact research library in Pytorch designed to study (Overcomplete)-Dictionary learning methods to extract concepts from large Vision models. In addition, this repository also introduces various visualization methods, attribution and metrics. However, Overcomplete emphasizes experimentation.

🚀 Getting Started with Overcomplete

Overcomplete requires Python 3.8 or newer and several dependencies, including Numpy. It supports both only Torch. Installation is straightforward with Pypi:

pip install overcomplete

With Overcomplete installed, you can dive into any optimisation based dictionary learning method to extract visual features. The API is designed to be intuitive, requiring only a few hyperparameters to get started.

Example usage:

import torch
import overcomplete
todo

Notebooks

  • Starter Notebook: Open in Google Colab

Citation

@article{todo,
}

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