  Deep Neural Networks are known to be brittle to even minor distribution
shifts compared to the training distribution. While one line of work has
demonstrated that Simplicity Bias (SB) of DNNs - bias towards learning only the
simplest features - is a key reason for this brittleness, another recent line
of work has surprisingly found that diverse/ complex features are indeed
learned by the backbone, and their brittleness is due to the linear
classification head relying primarily on the simplest features. To bridge the
gap between these two lines of work, we first hypothesize and verify that while
SB may not altogether preclude learning complex features, it amplifies simpler
features over complex ones. Namely, simple features are replicated several
times in the learned representations while complex features might not be
replicated. This phenomenon, we term Feature Replication Hypothesis, coupled
with the Implicit Bias of SGD to converge to maximum margin solutions in the
feature space, leads the models to rely mostly on the simple features for
classification. To mitigate this bias, we propose Feature Reconstruction
Regularizer (FRR) to ensure that the learned features can be reconstructed back
from the logits. The use of {\em FRR} in linear layer training (FRR-L)
encourages the use of more diverse features for classification. We further
propose to finetune the full network by freezing the weights of the linear
layer trained using FRR-L, to refine the learned features, making them more
suitable for classification. Using this simple solution, we demonstrate up to
15% gains in OOD accuracy on the recently introduced semi-synthetic datasets
with extreme distribution shifts. Moreover, we demonstrate noteworthy gains
over existing SOTA methods on the standard OOD benchmark DomainBed as well.
