Several Active Learning (AL) policies require retraining a target model several times in order to identify the most informative samples and rarely offer the option to focus on the acquisition of samples from underrepresented classes. Here the Mining of Single-Class by Active Learning (MiSiCAL) paradigm is introduced where an AL policy is constructed through deep reinforcement learning and exploits quantity-accuracy correlations to build datasets on which high-performance models can be trained with regards to specific classes. MiSiCAL is especially helpful in the case of very large batch sizes since it does not require repeated model training sessions as is common in other AL methods. This is thanks to its ability to exploit fixed representations of the candidate data points. We find that MiSiCAL is able to outperform a random policy on 150 out of 171 COCO10k classes, while the strongest baseline only outperforms random on 101 classes.

翻译：多个主动学习策略需要反复重新训练目标模型以识别最具信息量的样本，且很少提供从代表性不足的类别中获取样本的聚焦选项。本文提出基于主动学习的单类挖掘范式（MiSiCAL），该范式通过深度强化学习构建主动学习策略，利用数量-精度相关性来构建针对特定类别可训练高性能模型的数据集。由于能够利用候选数据点的固定表示，MiSiCAL无需像其他主动学习方法那样重复进行模型训练，尤其适用于超大批次场景。实验表明，在COCO10k数据集的171个类别中，MiSiCAL在150个类别上优于随机策略，而最强基线仅在101个类别上超越随机策略。