When data is streaming from multiple sources, conventional training methods update model weights often assuming the same level of reliability for each source; that is: a model does not consider data quality of each source during training. In many applications, sources can have varied levels of noise or corruption that has negative effects on the learning of a robust deep learning model. A key issue is that the quality of data or labels for individual sources is often not available during training and could vary over time. Our solution to this problem is to consider the mistakes made while training on data originating from sources and utilise this to create a perceived data quality for each source. This paper demonstrates a straight-forward and novel technique that can be applied to any gradient descent optimiser: Update model weights as a function of the perceived reliability of data sources within a wider data set. The algorithm controls the plasticity of a given model to weight updates based on the history of losses from individual data sources. We show that applying this technique can significantly improve model performance when trained on a mixture of reliable and unreliable data sources, and maintain performance when models are trained on data sources that are all considered reliable. All code to reproduce this work's experiments and implement the algorithm in the reader's own models is made available.

翻译：当数据从多种来源流出时,常规培训方法更新模型重量时往往假定每个来源的可靠性水平相同;即:模型不考虑每个来源在培训期间的数据质量;在许多应用中,源可能具有不同程度的噪音或腐败,对学习强有力的深层学习模型有负面影响;关键问题是,单个来源的数据或标签的质量在培训期间往往得不到,而且可能随时间而变化。我们解决这个问题的办法是,在培训来源的数据时考虑错误,并利用这种培训为每个来源创造一种感知的数据质量。本文展示了一种直向前进的和新的技术,可以应用于任何梯度下降选择器:更新模型重量,作为在更广泛的数据集中数据来源的感知可靠性的函数。算法控制了根据单个数据源损失历史给定的模型或标记更新重量的可塑性。我们表明,应用这一技术可以大大改进模型在培训可靠和不可靠数据来源时的性能,并在对模型进行所有被认为可靠的数据源培训时保持性能。所有复制工作模型的代码都在自己的模型中进行。