Robust early-learning: Hindering the memorization of noisy labels

By Xiaobo Xia, Tongliang Liu, Bo Han, Chen Gong, Nannan Wang, ZongYuan Ge, Yi Chang. International Conference on Learning Representations, 2021

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Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations (2021, background): “…labels [Liu and Tao, 2015], propose robust loss functions [Natarajan et al., 2013, Ghosh et al., 2017, Zhang and Sabuncu, 2018, Amid et al., 2019, Wang et al., 2019, Liu and Guo, 2020], or introduce a robust regularizer [Liu et al., 2020, Xia et al., 2020a, Cheng et al., 2021, Wei et al., 2021].”
Understanding and Improving Early Stopping for Learning with Noisy Labels (2021, background): “Following previous works [10, 33, 17, 31], symmetric noise is generated by uniformly ﬂipping labels for a percentage of the training dataset to all possible labels.”
The Curious Case of Hallucinations in Neural Machine Translation (2021, unknown): “Robust Early learning (Xia et al., 2021) that are designed to prevent”
On Memorization in Probabilistic Deep Generative Models (2021, background): “We argue, however, that memorization in supervised learning differs fundamentally from that in generative models, as the label prediction task affects the training dynamics, and label noise is known to induce memorization in supervised learning [23, 24].”