PBES: PCA Based Exemplar Sampling Algorithm for Continual Learning
This addresses the challenge of efficient data sampling for continual learning, though it appears incremental as it builds on existing PCA and sampling techniques.
The authors tackled the problem of exemplar selection in class-incremental learning by proposing a PCA-based method with median sampling, achieving better performance than state-of-the-art methods.
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.