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Unsupervised Domain Adaptation On Imagenet C

Metrics

mean Corruption Error (mCE)

Results

Performance results of various models on this benchmark

		Paper Title	Repository
ResNet50 (baseline), BatchNorm Adaptation, 8 samples	65.0	Improving robustness against common corruptions by covariate shift adaptation
ResNet50 (baseline), BatchNorm Adaptation, full adaptation	62.2	Improving robustness against common corruptions by covariate shift adaptation
ResNet50 + ENT	51.6	If your data distribution shifts, use self-learning
ResNet50 + RPL	50.5	If your data distribution shifts, use self-learning
ResNet50+DeepAug+AugMix, BatchNorm Adaptation, 8 samples	48.4	Improving robustness against common corruptions by covariate shift adaptation
ResNet50+DeepAug+AugMix, BatchNorm Adaptation, full adaptation	45.4	Improving robustness against common corruptions by covariate shift adaptation
ResNeXt101 32x8d + ENT	44.3	If your data distribution shifts, use self-learning
ResNeXt101 32x8d + RPL	43.2	If your data distribution shifts, use self-learning
ResNeXt101 32x8d + IG-3.5B + RPL	40.9	If your data distribution shifts, use self-learning
ResNeXt101 32x8d + IG-3.5B + ENT	40.8	If your data distribution shifts, use self-learning
ResNeXt101+DeepAug+AugMix, BatchNorm Adaptation, 8 samples	40.7	Improving robustness against common corruptions by covariate shift adaptation
ResNeXt101+DeepAug+AugMix, BatchNorm Adaptation, full adaptation	38.0	Improving robustness against common corruptions by covariate shift adaptation
ResNeXt101 32x8d + DeepAug + Augmix + ENT	35.5	If your data distribution shifts, use self-learning
ResNeXt101 32x8d + DeepAug + Augmix + RPL	34.8	If your data distribution shifts, use self-learning
EfficientNet-L2+ENT	23.0	If your data distribution shifts, use self-learning
EfficientNet-L2+RPL	22.0	If your data distribution shifts, use self-learning

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Unsupervised Domain Adaptation On Imagenet C | SOTA | HyperAI