Unlearning Comparator, a visual tool to compare machine unlearning
I built Unlearning Comparator, a visual analytics toolkit to help researchers and developers compare how different machine unlearning methods work. It provides a unified workflow to test for accuracy, efficiency, and privacy. You can check out the live demo linked in the post, and the source code is on GitHub: https://github.com/gnueaj/Machine-Unlearning-Comparator Our accompanying paper is currently under review at IEEE TVCG. Happy to answer any questions and would love to hear your feedback!
In plain words
Unlearning Comparator is a visual analytics toolkit designed for researchers and developers to evaluate and compare different machine unlearning methods. It provides a unified workflow to test machine learning models across three key dimensions: accuracy, efficiency, and privacy. The tool helps users understand how various unlearning approaches perform and trade off against each other, with source code available on GitHub.
written from the facts on this page · September 2026
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