Synthetic data (SIM) drawn from simulators have emerged as a popular
alternative for training models where acquiring annotated real-world images is
difficult. However, transferring models trained on synthetic images to
real-world applications can be challenging due to appearance disparities. A
commonly employed solution to counter this SIM2REAL gap is unsupervised domain
adaptation, where models are trained using labeled SIM data and unlabeled REAL
data. Mispredictions made by such SIM2REAL adapted models are often associated
with miscalibration - stemming from overconfident predictions on real data. In
this paper, we introduce AUGCAL, a simple training-time patch for unsupervised
adaptation that improves SIM2REAL adapted models by - (1) reducing overall
miscalibration, (2) reducing overconfidence in incorrect predictions and (3)
improving confidence score reliability by better guiding misclassification
detection - all while retaining or improving SIM2REAL performance. Given a base
SIM2REAL adaptation algorithm, at training time, AUGCAL involves replacing
vanilla SIM images with strongly augmented views (AUG intervention) and
additionally optimizing for a training time calibration loss on augmented SIM
predictions (CAL intervention). We motivate AUGCAL using a brief analytical
justification of how to reduce miscalibration on unlabeled REAL data. Through
our experiments, we empirically show the efficacy of AUGCAL across multiple
adaptation methods, backbones, tasks and shifts.

通过使用强化视图和在对合成图片预测的过程中优化训练时间校准损失，AUGCAL 可提高基于合成数据适应模型的性能，并降低在真实应用中的过度自信和错误预测的错误率。