Attentional Biased Stochastic Gradient for Imbalanced Classification

In this paper~\footnote{The original title is "Momentum SGD with Robust Weighting For Imbalanced Classification"}, we present a simple yet effective method (ABSGD) for addressing the data imbalance issue in deep learning. Our method is a simple modification to momentum SGD where we leverage an attentional mechanism to assign an individual importance weight to each gradient in the mini-batch. Unlike existing individual weighting methods that learn the individual weights by meta-learning on a separate balanced validation data, our weighting scheme is self-adaptive and is grounded in distributionally robust optimization. The weight of a sampled data is systematically proportional to exponential of a scaled loss value of the data, where the scaling factor is interpreted as the regularization parameter in the framework of information-regularized distributionally robust optimization. We employ a step damping strategy for the scaling factor to balance between the learning of feature extraction layers and the learning of the classifier layer. Compared with exiting meta-learning methods that require three backward propagations for computing mini-batch stochastic gradients at three different points at each iteration, our method is more efficient with only one backward propagation at each iteration as in standard deep learning methods. Compared with existing class-level weighting schemes, our method can be applied to online learning without any knowledge of class prior, while enjoying further performance boost in offline learning combined with existing class-level weighting schemes. Our empirical studies on several benchmark datasets also demonstrate the effectiveness of our proposed method