Our method learns confidence functions tailored for efficient and reliable auto-labeling. Using these in TBAL boosts the no. of auto-labeled points by up to 60% (while making < 5% auto-labeling errors) compared to baselines like softmax and several training-time and post-hoc calibration techniques.
