论文标题
MetainFonet：学习任务指导的信息用于样本重新加权
MetaInfoNet: Learning Task-Guided Information for Sample Reweighting
论文作者
论文摘要
深度神经网络已显示出可容易地过度拟合，以降低标签噪声或类失衡的偏向训练数据。元学习算法通常是为了以样本重量的形式来缓解此问题，通过学习一个将培训损失作为输入以产生样品权重的方式来减轻此问题。在本文中，我们提倡为元加权网络选择适当的输入对于特定任务中所需的样品权重至关重要，而训练损失并不总是正确的答案。鉴于此，我们提出了一种新型的元学习算法MetainFonet，该算法通过通过信息瓶颈策略强调与任务相关的信息来自动学习有效的表示作为元加权网络的输入。具有标签噪声或类不平衡的基准数据集上的广泛实验结果表明，元富其中的方法优于许多最先进的方法。
Deep neural networks have been shown to easily overfit to biased training data with label noise or class imbalance. Meta-learning algorithms are commonly designed to alleviate this issue in the form of sample reweighting, by learning a meta weighting network that takes training losses as inputs to generate sample weights. In this paper, we advocate that choosing proper inputs for the meta weighting network is crucial for desired sample weights in a specific task, while training loss is not always the correct answer. In view of this, we propose a novel meta-learning algorithm, MetaInfoNet, which automatically learns effective representations as inputs for the meta weighting network by emphasizing task-related information with an information bottleneck strategy. Extensive experimental results on benchmark datasets with label noise or class imbalance validate that MetaInfoNet is superior to many state-of-the-art methods.
