Open-Sampling: Exploring Out-of-Distribution data for Re-balancing Long-tailed datasets

• URL: http://arxiv.org/abs/2206.08802v1
• Date: Fri, 17 Jun 2022 14:29:52 GMT
• Title: Open-Sampling: Exploring Out-of-Distribution data for Re-balancing Long-tailed datasets
• Authors: Hongxin Wei, Lue Tao, Renchunzi Xie, Lei Feng, Bo An
• Abstract summary: 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 in a semi-supervised manner would harm the generalization performance. We propose a novel method called Open-sampling, which utilizes open-set noisy labels to re-balance the class priors of the training dataset.
• Score: 24.551465814633325
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: 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.

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