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Open-Sampling: Exploring Out-of-Distribution data for Re-balancing Long-tailed datasets
Hongxin Wei · Lue Tao · RENCHUNZI XIE · LEI FENG · Bo An

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Deep neural networks usually perform poorly when the training dataset suffers from extreme class imbalance. Recent studies found that directly training with out-of-distribution data (i.e., open-set samples) in a semi-supervised manner would harm the generalization performance.In this work, we theoretically show that out-of-distribution data can still be leveraged to augment the minority classes from a Bayesian perspective. Based on this motivation, we propose a novel method called Open-sampling, which utilizes open-set noisy labels to re-balance the class priors of the training dataset. For each open-set instance, the label is sampled from our pre-defined distribution that is complementary to the distribution of original class priors.We empirically show that Open-sampling not only re-balances the class priors but also encourages the neural network to learn separable representations.Extensive experiments demonstrate that our proposed method significantly outperforms existing data re-balancing methods and can boost the performance of existing state-of-the-art methods.

Author Information

Hongxin Wei (Nanyang Technological University)
Lue Tao (Nanjing University of Aeronautics and Astronautics)
RENCHUNZI XIE (Nanyang Technological University)
LEI FENG (Nanyang Technological University)
Bo An (Nanyang Technological University)

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