This paper presents simple and efficient methods to mitigate sampling bias in
active learning while achieving state-of-the-art accuracy and model robustness.
We introduce supervised contrastive active learning by leveraging the
contrastive loss for active learning under a supervised setting. We propose an
unbiased query strategy that selects informative data samples of diverse
feature representations with our methods: supervised contrastive active
learning (SCAL) and deep feature modeling (DFM). We empirically demonstrate our
proposed methods reduce sampling bias, achieve state-of-the-art accuracy and
model calibration in an active learning setup with the query computation 26x
faster than Bayesian active learning by disagreement and 11x faster than
CoreSet. The proposed SCAL method outperforms by a big margin in robustness to
dataset shift and out-of-distribution.

该论文提出了简单而高效的方法来减轻主动学习中的采样偏差，同时实现最先进的精度和模型鲁棒性。通过引入有监督对比主动学习和选择具有多样的特性表示的信息性数据样本的无偏查询策略，我们的方法：SCAL 和 DFM，实验表明，所提出的方法降低了采样偏差，在主动学习设置中，查询计算速度比基于贝叶斯的对抗式学习快 26 倍，比 CoreSet 快 11 倍。所提出的 SCAL 方法在对抗性数据集转移和预测真实分布方面具有显着的优势。