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Real-world object classes appear in imbalanced ratios. This poses asignificant challenge for classifiers which get biased towards frequentclasses. We hypothesize that improving the generalization capability of aclassifier should improve learning on imbalanced datasets. Here, we introducethe first hybrid loss function that jointly performs classification andclustering in a single formulation. Our approach is based on an `affinitymeasure' in Euclidean space that leads to the following benefits: (1) directenforcement of maximum margin constraints on classification boundaries, (2) atractable way to ensure uniformly spaced and equidistant cluster centers, (3)flexibility to learn multiple class prototypes to support diversity anddiscriminability in feature space. Our extensive experiments demonstrate thesignificant performance improvements on visual classification and verificationtasks on multiple imbalanced datasets. The proposed loss can easily be pluggedin any deep architecture as a differentiable block and demonstrates robustnessagainst different levels of data imbalance and corrupted labels.

Max-margin Class Imbalanced Learning with Gaussian Affinity