This paper introduces supervised contrastive active learning (SCAL) by leveraging the contrastive loss for active learning in a supervised setting. We propose efficient query strategies in active learning to select unbiased and informative data samples of diverse feature representations. We demonstrate our proposed method reduces sampling bias, achieves state-of-the-art accuracy and model calibration in an active learning setup with the query computation 11x faster than CoreSet and 26x faster than Bayesian active learning by disagreement. Our method yields well-calibrated models even with imbalanced datasets. We also evaluate robustness to dataset shift and out-of-distribution in active learning setup and demonstrate our proposed SCAL method outperforms high performing compute-intensive methods by a bigger margin (average 8.9% higher AUROC for out-of-distribution detection and average 7.2% lower ECE under dataset shift).
翻译:本文通过利用在受监督的环境中积极学习的对比性损失,引入了受监督的对比性积极学习(SCAL) 。我们提出了在积极学习中有效的查询战略,以选择不同特征的无偏见和资料性数据样本。我们展示了我们提出的方法,通过一个积极的学习结构,减少抽样偏差,实现最先进的精确度和模型校准,以查询计算速度比CoreSet快11x,比Bayesian通过分歧积极学习速度快26x11。我们的方法产生经过良好校正的模型,即使有不平衡的数据集。我们还评估了在积极学习结构中数据集转换和分配的稳健性,并展示了我们拟议的SCAL方法以更大的差幅(平均8.9%的AUROC用于分配外检测,平均7.2%的EC值低于数据转换)。