Striking the right balance with uncertainty

Learning unbiased models on imbalanced datasets is a significant challenge. Rare classes tend to get a concentrated
representation in the classification space which hampers the generalization of learned boundaries to new test
examples. In this paper, we demonstrate that the Bayesian uncertainty estimates directly correlate with the rarity of
classes and the difficulty level of individual samples. Subsequently, we present a novel framework for uncertainty
based class imbalance learning that follows two key insights: First, classification boundaries should be extended
further away from a more uncertain (rare) class to avoid over-fitting and enhance its generalization. Second, each
sample should be modeled as a multi-variate Gaussian distribution with a mean vector and a covariance matrix defined
by the sample’s uncertainty. The learned boundaries should respect not only the individual samples but also their
distribution in the feature space. Our proposed approach efficiently utilizes sample and class uncertainty information
to learn robust features and more generalizable classifiers.
We systematically study the class imbalance problem and derive a novel loss formulation for max-margin learning
based on Bayesian uncertainty measure. The proposed method shows significant performance improvements on six
benchmark datasets for face verification, attribute prediction, digit/object classification and skin lesion detection.