3 papers
cs.LG2024
Trained Models Tell Us How to Make Them Robust to Spurious Correlation without Group Annotation
Mahdi Ghaznavi, Hesam Asadollahzadeh, Fahimeh Hosseini Noohdani +5
Classifiers trained with Empirical Risk Minimization (ERM) tend to rely on attributes that have high spurious correlation with the target. This can degrade the performance on under…
cs.CV2024
Decompose-and-Compose: A Compositional Approach to Mitigating Spurious Correlation
Fahimeh Hosseini Noohdani, Parsa Hosseini, Aryan Yazdan Parast +2
While standard Empirical Risk Minimization (ERM) training is proven effective for image classification on in-distribution data, it fails to perform well on out-of-distribution samp…
cs.CV2023
Annotation-Free Group Robustness via Loss-Based Resampling
Mahdi Ghaznavi, Hesam Asadollahzadeh, HamidReza Yaghoubi Araghi +3
It is well-known that training neural networks for image classification with empirical risk minimization (ERM) makes them vulnerable to relying on spurious attributes instead of ca…