  Modern machine learning datasets can have biases for certain representations
that are leveraged by algorithms to achieve high performance without learning
to solve the underlying task. This problem is referred to as "representation
bias". The question of how to reduce the representation biases of a dataset is
investigated and a new dataset REPresentAtion bIas Removal (REPAIR) procedure
is proposed. This formulates bias minimization as an optimization problem,
seeking a weight distribution that penalizes examples easy for a classifier
built on a given feature representation. Bias reduction is then equated to
maximizing the ratio between the classification loss on the reweighted dataset
and the uncertainty of the ground-truth class labels. This is a minimax problem
that REPAIR solves by alternatingly updating classifier parameters and dataset
resampling weights, using stochastic gradient descent. An experimental set-up
is also introduced to measure the bias of any dataset for a given
representation, and the impact of this bias on the performance of recognition
models. Experiments with synthetic and action recognition data show that
dataset REPAIR can significantly reduce representation bias, and lead to
improved generalization of models trained on REPAIRed datasets. The tools used
for characterizing representation bias, and the proposed dataset REPAIR
algorithm, are available at https://github.com/JerryYLi/Dataset-REPAIR/.
