Title: Instance Correction for Learning with Open-set Noisy Labels Show Chinese title

Title: 实例纠正用于开放集噪声标签学习

Abstract: The problem of open-set noisy labels denotes that part of training data have a different label space that does not contain the true class. Lots of approaches, e.g., loss correction and label correction, cannot handle such open-set noisy labels well, since they need training data and test data to share the same label space, which does not hold for learning with open-set noisy labels. The state-of-the-art methods thus employ the sample selection approach to handle open-set noisy labels, which tries to select clean data from noisy data for network parameters updates. The discarded data are seen to be mislabeled and do not participate in training. Such an approach is intuitive and reasonable at first glance. However, a natural question could be raised "can such data only be discarded during training?". In this paper, we show that the answer is no. Specifically, we discuss that the instances of discarded data could consist of some meaningful information for generalization. For this reason, we do not abandon such data, but use instance correction to modify the instances of the discarded data, which makes the predictions for the discarded data consistent with given labels. Instance correction are performed by targeted adversarial attacks. The corrected data are then exploited for training to help generalization. In addition to the analytical results, a series of empirical evidences are provided to justify our claims. Show Chinese abstract

Abstract: 开放集噪声标签的问题指的是部分训练数据具有不同的标签空间，该空间不包含真实类别。 许多方法，例如损失校正和标签校正，无法很好地处理这种开放集噪声标签，因为它们需要训练数据和测试数据共享相同的标签空间，而这一点在开放集噪声标签学习中并不成立。 因此，最先进的方法采用样本选择方法来处理开放集噪声标签，这种方法试图从噪声数据中选择干净的数据用于网络参数更新。 被丢弃的数据被视为错误标记，并不参与训练。 这种方法乍一看是直观且合理的。 然而，一个自然的问题可能会被提出 “这样的数据只能在训练期间被丢弃吗？” 在本文中，我们表明答案是否定的。 具体来说，我们讨论了被丢弃数据的实例可能包含有助于泛化的某些有意义的信息。 因此，我们不放弃这些数据，而是使用实例校正来修改被丢弃数据的实例，这使得被丢弃数据的预测与给定标签一致。 实例校正通过定向对抗攻击来执行。 然后利用校正后的数据进行训练以帮助泛化。 除了分析结果外，还提供了一系列实证证据来证明我们的主张。