  Deep neural networks can be unreliable in the real world especially when they
heavily use spurious features for their predictions. Recently, Singla & Feizi
(2022) introduced the Salient Imagenet dataset by annotating and localizing
core and spurious features of ~52k samples from 232 classes of Imagenet. While
this dataset is useful for evaluating the reliance of pretrained models on
spurious features, its small size limits its usefulness for training models. In
this work, we first introduce the Salient Imagenet-1M dataset with more than 1
million soft masks localizing core and spurious features for all 1000 Imagenet
classes. Using this dataset, we first evaluate the reliance of several Imagenet
pretrained models (42 total) on spurious features and observe that: (i)
transformers are more sensitive to spurious features compared to Convnets, (ii)
zero-shot CLIP transformers are highly susceptible to spurious features. Next,
we introduce a new learning paradigm called Core Risk Minimization (CoRM) whose
objective ensures that the model predicts a class using its core features. We
evaluate different computational approaches for solving CoRM and achieve
significantly higher (+12%) core accuracy (accuracy when non-core regions
corrupted using noise) with no drop in clean accuracy compared to models
trained via Empirical Risk Minimization.
