Mitigating Annotation Burden in Active Learning with Transfer Learning and Iterative Acquisition Functions

In situations where there is a lack of readily available annotated data, active learning is a useful tactic. To increase the model’s generalization, it entails training a model on a constrained annotation budget and repeatedly choosing the best data points for additional annotation. Active learning in deep learning usually necessitates fine-tuning subsequent deep models. But this has drawbacks, too. Specifically, it requires an initially large batch of annotated data, which is feasible when there is a constrained budget for annotation in general. We challenge this problem with a strategy inspired by transfer learning, in which only shallow classifiers are taught during active learning iterations, when an already expert model is used, it functions as a feature extractor. We also suggest a new acquisition function that takes use of active learning’s iterative character. To improve robustness, this function chooses samples based on the largest change in uncertainty between the last two models’ predictions. To ensure representation, we incorporate a diversification stage where we select samples from various regions within the categorization space. Using balanced and imbalanced datasets, our strategy is compared to competitor approaches and shows superior performance in minimizing annotation burden while preserving good model accuracy.