  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.
