Benchmarking Test-Time Adaptation against Distribution Shifts in Image Classification

TL;DR

This paper introduces a comprehensive benchmark for evaluating 13 test-time adaptation methods across multiple image classification datasets and network architectures, addressing the need for fair and consistent comparison of robustness techniques under distribution shifts.

Contribution

The paper presents a unified PyTorch framework and systematic evaluation of diverse TTA methods on various datasets and backbones, filling a gap in standardized benchmarking.

Findings

Different TTA methods vary significantly in performance.

Compatibility of TTA methods with network backbones is analyzed.

Benchmark provides a reliable comparison platform for future research.

Abstract

Test-time adaptation (TTA) is a technique aimed at enhancing the generalization performance of models by leveraging unlabeled samples solely during prediction. Given the need for robustness in neural network systems when faced with distribution shifts, numerous TTA methods have recently been proposed. However, evaluating these methods is often done under different settings, such as varying distribution shifts, backbones, and designing scenarios, leading to a lack of consistent and fair benchmarks to validate their effectiveness. To address this issue, we present a benchmark that systematically evaluates 13 prominent TTA methods and their variants on five widely used image classification datasets: CIFAR-10-C, CIFAR-100-C, ImageNet-C, DomainNet, and Office-Home. These methods encompass a wide range of adaptation scenarios (e.g. online adaptation v.s. offline adaptation, instance adaptation v.s. batch adaptation v.s. domain adaptation). Furthermore, we explore the compatibility of different TTA methods with diverse network backbones. To implement this benchmark, we have developed a unified framework in PyTorch, which allows for consistent evaluation and comparison of the TTA methods across the different datasets and network architectures. By establishing this benchmark, we aim to provide researchers and practitioners with a reliable means of assessing and comparing the effectiveness of TTA methods in improving model robustness and generalization performance. Our code is available at https://github.com/yuyongcan/Benchmark-TTA.