Methodological Guideline for Smart Data Management in an Iterative Context

This reports documents methods, principles and recommendations for two forms of incremental dataset construction: active learning and class incremental learning.
The former aims to allocate an annotation budget on an initially unlabled dataset.
The latter deals with scenarios where the dataset is updated with new batches of data containing new classes. It aims to maximize the model performance on old and new classes under different data retention constraints.