Benchmarking Spurious Bias in Few-Shot Image Classifiers

We propose a systematic and rigorous benchmark framework, termed FewSTAB, to fairly demonstrate and quantify varied degrees of robustness of few-shot classifiers to spurious bias. FewSTAB creates few-shot evaluation tasks with biased attributes so that using them for predictions can demonstrate poor performance.

Learning Robust Classifiers with Self-Guided Spurious Correlation Mitigation

We tackle an annotation-free setting and propose a self-guided spurious correlation mitigation framework. Our framework automatically constructs fine-grained training labels tailored for a classifier obtained with empirical risk minimization to improve its robustness against spurious correlations.

Spuriousness-Aware Meta-Learning for Learning Robust Classifiers

We propose a novel learning framework based on meta-learning, termed SPUME -- SPUriousness-aware MEta-learning for learning robust classifiers, to train an image classifier to be robust to spurious correlations.

AdvST: Revisiting Data Augmentations for Single Domain Generalization

We conceptualize standard data augmentations with learnable parameters as semantics transformations that can manipulate certain semantics of a sample, such as the geometry or color of an image. Then, we propose Adversarial learning with Semantics Transformations (AdvST) that augments the source domain data with semantics transformations and learns a robust model with the augmented data.