Real-Time Uncertainty Estimation in Computer Vision via Uncertainty-Aware Distribution Distillation

Calibrated estimates of uncertainty are critical for many real-world computer vision applications of deep learning. While there are several widely-used uncertainty estimation methods, dropout inference [11] stands out for its simplicity and efficacy. This technique, however, requires multiple forward passes through the network during inference and therefore can be too resource-intensive to be depl...