  Can we modify the training data distribution to encourage the underlying
optimization method toward finding solutions with superior generalization
performance on in-distribution data? In this work, we approach this question
for the first time by comparing the inductive bias of gradient descent (GD)
with that of sharpness-aware minimization (SAM). By studying a two-layer CNN,
we prove that SAM learns easy and difficult features more uniformly,
particularly in early epochs. That is, SAM is less susceptible to simplicity
bias compared to GD. Based on this observation, we propose USEFUL, an algorithm
that clusters examples based on the network output early in training and
upsamples examples with no easy features to alleviate the pitfalls of the
simplicity bias. We show empirically that modifying the training data
distribution in this way effectively improves the generalization performance on
the original data distribution when training with (S)GD by mimicking the
training dynamics of SAM. Notably, we demonstrate that our method can be
combined with SAM and existing data augmentation strategies to achieve, to the
best of our knowledge, state-of-the-art performance for training ResNet18 on
CIFAR10, STL10, CINIC10, Tiny-ImageNet; ResNet34 on CIFAR100; and VGG19 and
DenseNet121 on CIFAR10.
