2021/06/07 by Tiago Salvador, Salvador, Tiago, Vikram Voleti +6
Computer Science · Engineering · Mathematics · #Adaptation (eye) #Algorithm #Artificial intelligence #Calibration #Computer science #Data mining #Distribution (mathematics) #Domain (mathematical analysis) #Domain Adaptation and Few-Shot Learning #Engineering #Estimation #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Image (mathematics) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine learning #Mathematics #Multimodal Machine Learning Applications #Pattern recognition (psychology) #Probability distribution #Range (aeronautics) #Simple (philosophy) #Statistics #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.2106.03762
published in arXiv (Cornell University) (Cornell University) · 17 pages, 4 Tables, 9 Figures
openalex publication_date 2021/06/07 · arxiv created 2021/10/17 · arxiv updated 2021/10/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Distribution shift is an important concern in deep image classification, produced either by corruption of the source images, or a complete change, with the solution involving domain adaptation. While the primary goal is to improve accuracy under distribution shift, an important secondary goal is uncertainty estimation: evaluating the probability that the prediction of a model is correct. While improving accuracy is hard, uncertainty estimation turns out to be frustratingly easy. Prior works have appended uncertainty estimation into the model and training paradigm in various ways. Instead, we show that we can estimate uncertainty by simply exposing the original model to corrupted images, and performing simple statistical calibration on the image outputs. Our frustratingly easy methods demonstrate superior performance on a wide range of distribution shifts as well as on unsupervised domain adaptation tasks, measured through extensive experimentation.