Qualitative Image Selection with Active Learning
In this work, an active learning pipeline is presented that allows for comparative tests between 3 different types of data scoring methods, uncertainty selection, diversity selection, and loss selection. Tests and parameter sweeps indicate that hyperparameters like reshuffling and early stopping are required to ensure fair comparisons. We then apply our pipeline to test some naive scoring methods