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SotA/Methodology/Domain Adaptation/ImageNet-C

Domain Adaptation on ImageNet-C

Metric: Top 1 Accuracy (higher is better)

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#Model↕Top 1 Accuracy▼AugmentationsPaperDate↕Code
1FAN-L-Hybrid (IN-22k)73.6YesUnderstanding The Robustness in Vision Transform...2022-04-26Code
2FAN-B-Hybrid (IN-22k)70.5YesUnderstanding The Robustness in Vision Transform...2022-04-26Code
3ResNet-50 (PushPull-Conv) + PRIME69.4NoPushPull-Net: Inhibition-driven ResNet robust to...2024-08-07Code
4FAN-L-Hybrid+STL69.2NoFully Attentional Networks with Self-emerging To...2024-01-08Code
5FAN-L-Hybrid67.7NoUnderstanding The Robustness in Vision Transform...2022-04-26Code
6DiffAUD (ConvNeXt-Tiny)64.3Yes--Code
7DiffAUD (Swin-Tiny)61Yes--Code
8PRIME + DeepAugment (ResNet-50)59.9NoPRIME: A few primitives can boost robustness to ...2021-12-27Code
9ViT-B/16-SAM56.5NoWhen Vision Transformers Outperform ResNets with...2021-06-03Code
10PRIME with JSD (ResNet-50)56.4NoPRIME: A few primitives can boost robustness to ...2021-12-27Code
11PRIME (ResNet-50)55NoPRIME: A few primitives can boost robustness to ...2021-12-27Code
12ResNet-152x2-SAM55NoWhen Vision Transformers Outperform ResNets with...2021-06-03Code
13DiffAUD (ResNet-50)52.1Yes--Code
14Mixer-B/8-SAM48.9NoWhen Vision Transformers Outperform ResNets with...2021-06-03Code