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May, 2023
回声:通过假偏倚标记在回声室中进行无监督去偏置化
Echoes: Unsupervised Debiasing via Pseudo-bias Labeling in an Echo Chamber
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Rui Hu, Yahan Tu, Jitao Sang
TL;DR
本文研究神经网络在训练数据偏差时如何进行鲁棒训练,并提出了一种名为回声方法的策略,该方法训练偏向模型和目标模型以解决偏向模型训练中过拟合的问题,并在合成和真实数据集上取得了优越的去偏成果。
Abstract
neural networks
often learn spurious correlations when exposed to
biased training data
, leading to poor performance on out-of-distribution data. A biased dataset can be divided, according to biased features, into
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