Annotation-Free Group Robustness via Loss-Based Resampling
Mahdi Ghaznavi Hesam Asadollahzadeh HamidReza Yaghoubi Araghi Fahimeh Hosseini Noohdani Mohammad Hossein Rohban Mahdieh Soleymani Baghshah
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 causal ones for prediction. Previously, deep feature
re-weighting (DFR) has proposed retraining the last layer of a pre-trained
network on balanced data concerning spurious attributes, making it robust to
spurious correlation. However, spurious attribute annotations are not always
available. In order to provide group robustness without such annotations, we
propose a new method, called loss-based feature re-weighting (LFR), in which we
infer a grouping of the data by evaluating an ERM-pre-trained model on a small
left-out split of the training data. Then, a balanced number of samples is
chosen by selecting high-loss samples from misclassified data points and
low-loss samples from correctly-classified ones. Finally, we retrain the last
layer on the selected balanced groups to make the model robust to spurious
correlation. For a complete assessment, we evaluate LFR on various versions of
Waterbirds and CelebA datasets with different spurious correlations, which is a
novel technique for observing the model's performance in a wide range of
spuriosity rates. While LFR is extremely fast and straightforward, it
outperforms the previous methods that do not assume group label availability,
as well as the DFR with group annotations provided, in cases of high spurious
correlation in the training data.