Single Shot Active Learning using Pseudo Annotators
This addresses the challenge of active learning in scenarios with limited human expert availability, though it is incremental as it builds on existing active learning frameworks.
The paper tackles the problem of single-shot active learning where human annotations are not available during sample selection, proposing a method that uses random labeling from pseudo annotators to enable exploratory behavior. Experiments on real-world datasets show that the proposed method outperforms state-of-the-art approaches.
Standard myopic active learning assumes that human annotations are always obtainable whenever new samples are selected. This, however, is unrealistic in many real-world applications where human experts are not readily available at all times. In this paper, we consider the single shot setting: all the required samples should be chosen in a single shot and no human annotation can be exploited during the selection process. We propose a new method, Active Learning through Random Labeling (ALRL), which substitutes single human annotator for multiple, what we will refer to as, pseudo annotators. These pseudo annotators always provide uniform and random labels whenever new unlabeled samples are queried. This random labeling enables standard active learning algorithms to also exhibit the exploratory behavior needed for single shot active learning. The exploratory behavior is further enhanced by selecting the most representative sample via minimizing nearest neighbor distance between unlabeled samples and queried samples. Experiments on real-world datasets demonstrate that the proposed method outperforms several state-of-the-art approaches.