De-Biasing Neural Networks With Estimated Offset for Class Imbalanced Learning

Byungju Kim, Hyeong Gwon Hong, Junmo Kim; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2021, pp. 1479-1487

The imbalanced distribution of the training data makes the networks biased to the frequent classes. Existing methods to resolve the problem involve re-sampling, re-weighting, or cost-sensitive learning. Most of them anticipate that emphasizing the minority classes during the training would help the network to learn better representations. In this paper, we propose a method for reparameterizing softmax classifiers' offsets so that training is less sensitive to class imbalance. We first observe that the trained offset of the baseline linear classifier is biased toward the majority classes due to the imbalance. Instead of the trained offset, we define the estimated offset, and constrain it to be uniform over the classes. In experiments with long-tailed benchmarks, our method exhibits the best performance. These experiments verify that our proposed method effectively encourages the networks to learn better representations for minority classes while preserving the performance for the majority classes.