PBES: PCA Based Exemplar Sampling Algorithm for Continual Learning

We propose a novel exemplar selection approach based on Principal Component Analysis (PCA) and median sampling, and a neural network training regime in the setting of class-incremental learning. This approach avoids the pitfalls due to outliers in the data and is both simple to implement and use across various incremental machine learning models. It also has independent usage as a sampling algorithm. We achieve better performance compared to state-of-the-art methods. I. INTRODUCTION In continual learning (CL) a machine learning model continually keeps learning from new data and the data is viewed as a stream rather than a batch. A model in a CL system has to adapt to the new incoming data, and suffers from so-called catastrophic forgetting (CF) due to the inaccessibility of the data of earlier tasks.