TasksSotADatasetsPapersMethodsSubmitAbout
Papers With Code 2

A community resource for machine learning research: papers, code, benchmarks, and state-of-the-art results.

Explore

Notable BenchmarksAll SotADatasetsPapersMethods

Community

Submit ResultsAbout

Data sourced from the PWC Archive (CC-BY-SA 4.0). Built by the community, for the community.

Papers/Measuring Robustness to Natural Distribution Shifts in Ima...

Measuring Robustness to Natural Distribution Shifts in Image Classification

Rohan Taori, Achal Dave, Vaishaal Shankar, Nicholas Carlini, Benjamin Recht, Ludwig Schmidt

2020-07-01NeurIPS 2020 12Image ClassificationDomain GeneralizationGeneral ClassificationClassification
PaperPDFCode(official)

Abstract

We study how robust current ImageNet models are to distribution shifts arising from natural variations in datasets. Most research on robustness focuses on synthetic image perturbations (noise, simulated weather artifacts, adversarial examples, etc.), which leaves open how robustness on synthetic distribution shift relates to distribution shift arising in real data. Informed by an evaluation of 204 ImageNet models in 213 different test conditions, we find that there is often little to no transfer of robustness from current synthetic to natural distribution shift. Moreover, most current techniques provide no robustness to the natural distribution shifts in our testbed. The main exception is training on larger and more diverse datasets, which in multiple cases increases robustness, but is still far from closing the performance gaps. Our results indicate that distribution shifts arising in real data are currently an open research problem. We provide our testbed and data as a resource for future work at https://modestyachts.github.io/imagenet-testbed/ .

Results

TaskDatasetMetricValueModel
Domain AdaptationVizWiz-ClassificationAccuracy - All Images38.8ResNet-50 (IN-C)
Domain AdaptationVizWiz-ClassificationAccuracy - Clean Images42.9ResNet-50 (IN-C)
Domain AdaptationVizWiz-ClassificationAccuracy - Corrupted Images33.6ResNet-50 (IN-C)
Domain AdaptationVizWiz-ClassificationAccuracy - All Images38.8ResNet-50 (IN-C_brightness)
Domain AdaptationVizWiz-ClassificationAccuracy - Clean Images43.5ResNet-50 (IN-C_brightness)
Domain AdaptationVizWiz-ClassificationAccuracy - Corrupted Images32.5ResNet-50 (IN-C_brightness)
Domain AdaptationVizWiz-ClassificationAccuracy - All Images38.3ResNet-50 (IN-C_spatter)
Domain AdaptationVizWiz-ClassificationAccuracy - Clean Images42.7ResNet-50 (IN-C_spatter)
Domain AdaptationVizWiz-ClassificationAccuracy - Corrupted Images31.4ResNet-50 (IN-C_spatter)
Domain AdaptationVizWiz-ClassificationAccuracy - All Images38.2ResNet-50 (IN-C_saturate)
Domain AdaptationVizWiz-ClassificationAccuracy - Clean Images42.4ResNet-50 (IN-C_saturate)
Domain AdaptationVizWiz-ClassificationAccuracy - Corrupted Images32.4ResNet-50 (IN-C_saturate)
Domain AdaptationVizWiz-ClassificationAccuracy - All Images37.4ResNet-50 (IN-C_pixelate)
Domain AdaptationVizWiz-ClassificationAccuracy - Clean Images41.4ResNet-50 (IN-C_pixelate)
Domain AdaptationVizWiz-ClassificationAccuracy - Corrupted Images30.9ResNet-50 (IN-C_pixelate)
Domain AdaptationVizWiz-ClassificationAccuracy - All Images36.5ResNet-50 (IN-C_contrast)
Domain AdaptationVizWiz-ClassificationAccuracy - Clean Images40.9ResNet-50 (IN-C_contrast)
Domain AdaptationVizWiz-ClassificationAccuracy - Corrupted Images30.7ResNet-50 (IN-C_contrast)
Domain AdaptationVizWiz-ClassificationAccuracy - All Images36.5ResNet-50 (IN-C_jpeg_compression)
Domain AdaptationVizWiz-ClassificationAccuracy - Clean Images41.3ResNet-50 (IN-C_jpeg_compression)
Domain AdaptationVizWiz-ClassificationAccuracy - Corrupted Images30.3ResNet-50 (IN-C_jpeg_compression)
Domain AdaptationVizWiz-ClassificationAccuracy - All Images36.4ResNet-50 (IN-C_gaussian_noise)
Domain AdaptationVizWiz-ClassificationAccuracy - Clean Images40.6ResNet-50 (IN-C_gaussian_noise)
Domain AdaptationVizWiz-ClassificationAccuracy - Corrupted Images30.2ResNet-50 (IN-C_gaussian_noise)
Domain AdaptationVizWiz-ClassificationAccuracy - All Images36.1ResNet-50 (IN-C_frost)
Domain AdaptationVizWiz-ClassificationAccuracy - Clean Images40ResNet-50 (IN-C_frost)
Domain AdaptationVizWiz-ClassificationAccuracy - Corrupted Images29.7ResNet-50 (IN-C_frost)
Domain AdaptationVizWiz-ClassificationAccuracy - All Images35.9ResNet-50 (IN-C_fog_aws)
Domain AdaptationVizWiz-ClassificationAccuracy - Clean Images39.9ResNet-50 (IN-C_fog_aws)
Domain AdaptationVizWiz-ClassificationAccuracy - Corrupted Images30.3ResNet-50 (IN-C_fog_aws)
Domain AdaptationVizWiz-ClassificationAccuracy - All Images35.7ResNet-50 (IN-C_motion_blur)
Domain AdaptationVizWiz-ClassificationAccuracy - Clean Images39.6ResNet-50 (IN-C_motion_blur)
Domain AdaptationVizWiz-ClassificationAccuracy - Corrupted Images30.2ResNet-50 (IN-C_motion_blur)
Domain AdaptationVizWiz-ClassificationAccuracy - All Images32.7ResNet-50 (IN-C_zoom_blur)
Domain AdaptationVizWiz-ClassificationAccuracy - Clean Images36.6ResNet-50 (IN-C_zoom_blur)
Domain AdaptationVizWiz-ClassificationAccuracy - Corrupted Images28.3ResNet-50 (IN-C_zoom_blur)
Domain AdaptationVizWiz-ClassificationAccuracy - All Images30.2ResNet-50 (IN-C_greyscale)
Domain AdaptationVizWiz-ClassificationAccuracy - Clean Images34.3ResNet-50 (IN-C_greyscale)
Domain AdaptationVizWiz-ClassificationAccuracy - Corrupted Images24.3ResNet-50 (IN-C_greyscale)
Domain GeneralizationVizWiz-ClassificationAccuracy - All Images38.8ResNet-50 (IN-C)
Domain GeneralizationVizWiz-ClassificationAccuracy - Clean Images42.9ResNet-50 (IN-C)
Domain GeneralizationVizWiz-ClassificationAccuracy - Corrupted Images33.6ResNet-50 (IN-C)
Domain GeneralizationVizWiz-ClassificationAccuracy - All Images38.8ResNet-50 (IN-C_brightness)
Domain GeneralizationVizWiz-ClassificationAccuracy - Clean Images43.5ResNet-50 (IN-C_brightness)
Domain GeneralizationVizWiz-ClassificationAccuracy - Corrupted Images32.5ResNet-50 (IN-C_brightness)
Domain GeneralizationVizWiz-ClassificationAccuracy - All Images38.3ResNet-50 (IN-C_spatter)
Domain GeneralizationVizWiz-ClassificationAccuracy - Clean Images42.7ResNet-50 (IN-C_spatter)
Domain GeneralizationVizWiz-ClassificationAccuracy - Corrupted Images31.4ResNet-50 (IN-C_spatter)
Domain GeneralizationVizWiz-ClassificationAccuracy - All Images38.2ResNet-50 (IN-C_saturate)
Domain GeneralizationVizWiz-ClassificationAccuracy - Clean Images42.4ResNet-50 (IN-C_saturate)
Domain GeneralizationVizWiz-ClassificationAccuracy - Corrupted Images32.4ResNet-50 (IN-C_saturate)
Domain GeneralizationVizWiz-ClassificationAccuracy - All Images37.4ResNet-50 (IN-C_pixelate)
Domain GeneralizationVizWiz-ClassificationAccuracy - Clean Images41.4ResNet-50 (IN-C_pixelate)
Domain GeneralizationVizWiz-ClassificationAccuracy - Corrupted Images30.9ResNet-50 (IN-C_pixelate)
Domain GeneralizationVizWiz-ClassificationAccuracy - All Images36.5ResNet-50 (IN-C_contrast)
Domain GeneralizationVizWiz-ClassificationAccuracy - Clean Images40.9ResNet-50 (IN-C_contrast)
Domain GeneralizationVizWiz-ClassificationAccuracy - Corrupted Images30.7ResNet-50 (IN-C_contrast)
Domain GeneralizationVizWiz-ClassificationAccuracy - All Images36.5ResNet-50 (IN-C_jpeg_compression)
Domain GeneralizationVizWiz-ClassificationAccuracy - Clean Images41.3ResNet-50 (IN-C_jpeg_compression)
Domain GeneralizationVizWiz-ClassificationAccuracy - Corrupted Images30.3ResNet-50 (IN-C_jpeg_compression)
Domain GeneralizationVizWiz-ClassificationAccuracy - All Images36.4ResNet-50 (IN-C_gaussian_noise)
Domain GeneralizationVizWiz-ClassificationAccuracy - Clean Images40.6ResNet-50 (IN-C_gaussian_noise)
Domain GeneralizationVizWiz-ClassificationAccuracy - Corrupted Images30.2ResNet-50 (IN-C_gaussian_noise)
Domain GeneralizationVizWiz-ClassificationAccuracy - All Images36.1ResNet-50 (IN-C_frost)
Domain GeneralizationVizWiz-ClassificationAccuracy - Clean Images40ResNet-50 (IN-C_frost)
Domain GeneralizationVizWiz-ClassificationAccuracy - Corrupted Images29.7ResNet-50 (IN-C_frost)
Domain GeneralizationVizWiz-ClassificationAccuracy - All Images35.9ResNet-50 (IN-C_fog_aws)
Domain GeneralizationVizWiz-ClassificationAccuracy - Clean Images39.9ResNet-50 (IN-C_fog_aws)
Domain GeneralizationVizWiz-ClassificationAccuracy - Corrupted Images30.3ResNet-50 (IN-C_fog_aws)
Domain GeneralizationVizWiz-ClassificationAccuracy - All Images35.7ResNet-50 (IN-C_motion_blur)
Domain GeneralizationVizWiz-ClassificationAccuracy - Clean Images39.6ResNet-50 (IN-C_motion_blur)
Domain GeneralizationVizWiz-ClassificationAccuracy - Corrupted Images30.2ResNet-50 (IN-C_motion_blur)
Domain GeneralizationVizWiz-ClassificationAccuracy - All Images32.7ResNet-50 (IN-C_zoom_blur)
Domain GeneralizationVizWiz-ClassificationAccuracy - Clean Images36.6ResNet-50 (IN-C_zoom_blur)
Domain GeneralizationVizWiz-ClassificationAccuracy - Corrupted Images28.3ResNet-50 (IN-C_zoom_blur)
Domain GeneralizationVizWiz-ClassificationAccuracy - All Images30.2ResNet-50 (IN-C_greyscale)
Domain GeneralizationVizWiz-ClassificationAccuracy - Clean Images34.3ResNet-50 (IN-C_greyscale)
Domain GeneralizationVizWiz-ClassificationAccuracy - Corrupted Images24.3ResNet-50 (IN-C_greyscale)

Related Papers

Automatic Classification and Segmentation of Tunnel Cracks Based on Deep Learning and Visual Explanations2025-07-18Adversarial attacks to image classification systems using evolutionary algorithms2025-07-17Efficient Adaptation of Pre-trained Vision Transformer underpinned by Approximately Orthogonal Fine-Tuning Strategy2025-07-17Federated Learning for Commercial Image Sources2025-07-17MUPAX: Multidimensional Problem Agnostic eXplainable AI2025-07-17Simulate, Refocus and Ensemble: An Attention-Refocusing Scheme for Domain Generalization2025-07-17GLAD: Generalizable Tuning for Vision-Language Models2025-07-17MoTM: Towards a Foundation Model for Time Series Imputation based on Continuous Modeling2025-07-17