Loss Adapted Plasticity in Deep Neural Networks to Learn from Data with Unreliable Sources

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.