2021
Detecting Errors and Estimating Accuracy on Unlabeled Data with Self-training Ensembles
NeurIPS 2021poster
When a deep learning model is deployed in the wild, it can encounter test data drawn from distributions different from the training data distribution and suffer drop in performance. For safe deployment, it is essential to estimate the accuracy of the pre-trained model on the test data. However, the…