# Frustratingly Easy Uncertainty Estimation for Distribution Shift

Source: [Frustratingly Easy Uncertainty Estimation for Distribution Shift](<https://www.sota2.com/research/paper/68fa6b121e209131cea2bf53>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T23:39:16.015Z

Authors: Tiago Salvador, Vikram Voleti, Alexander Iannantuono, Adam Oberman

Published: 2021-06-07T16:27:18.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2106.03762>)

[PDF](<https://arxiv.org/pdf/2106.03762.pdf>)