SpuCo
SpuCo is a Python package developed to further research to address spurious correlations. Spurious correlations arise when machine learning models learn to exploit easy features that are not predictive of class membership but are correlated with a given class in the training data. This leads to catastrophically poor performance on the groups of data without such spurious features at test time.
The SpuCo package is designed to help researchers and practitioners evaluate the robustness of their machine learning algorithms against spurious correlations that may exist in real-world data. SpuCo provides:
Modular implementations of current SOTA to address spurious correlations
SpuCoMNIST: a controllable synthetic dataset that explores real-world data properties such as spurious feature difficulty, label noise and feature noise.
SpuCoAnimals: a large-scale vision dataset curated from ImageNet to explore real-world spurious correlations
Link to Paper: https://arxiv.org/abs/2306.11957 Link to GitHub: https://github.com/BigML-CS-UCLA/SpuCo
Note
This project is under active development.
Getting Started
User Guide
About Us
This package is maintained by Siddharth Joshi from the BigML group at UCLA headed by Professor Baharan Mirzasoleiman.