task_path stringlengths 3 199 ⌀ | dataset stringlengths 1 128 ⌀ | model_name stringlengths 1 223 ⌀ | paper_url stringlengths 21 601 ⌀ | metric_name stringlengths 1 50 ⌀ | metric_value stringlengths 1 9.22k ⌀ |
|---|---|---|---|---|---|
Domain Adaptation > Domain Generalization | ImageNet-A | CutMix (ResNet-50) | https://arxiv.org/abs/1905.04899v2 | Top-1 accuracy % | 7.3 |
Domain Adaptation > Domain Generalization | ImageNet-A | Mixup (ResNet-50) | http://arxiv.org/abs/1710.09412v2 | Top-1 accuracy % | 6.6 |
Domain Adaptation > Domain Generalization | ImageNet-A | Cutout (ResNet-50) | http://arxiv.org/abs/1708.04552v2 | Top-1 accuracy % | 4.4 |
Domain Adaptation > Domain Generalization | ImageNet-A | ResNet-50 (300 Epochs) | http://arxiv.org/abs/1512.03385v1 | Top-1 accuracy % | 4.2 |
Domain Adaptation > Domain Generalization | ImageNet-A | Stylized ImageNet (ResNet-50) | https://arxiv.org/abs/1811.12231v3 | Top-1 accuracy % | 2.3 |
Domain Adaptation > Domain Generalization | ImageNet-A | ResNet-50 | https://arxiv.org/abs/1907.07174v4 | Top-1 accuracy % | 0 |
Domain Adaptation > Domain Generalization | NICO Vehicle | NAS-OoD | https://arxiv.org/abs/2109.02038v1 | Accuracy | 81.59 |
Domain Adaptation > Domain Generalization | NICO Vehicle | DecAug (Resnet-18) | https://arxiv.org/abs/2012.09382v1 | Accuracy | 80.12 |
Domain Adaptation > Domain Generalization | NICO Vehicle | DRO (Resnet-18) | https://arxiv.org/abs/1911.08731v2 | Accuracy | 77.61 |
Domain Adaptation > Domain Generalization | NICO Vehicle | ResNet-18 | http://arxiv.org/abs/1903.06864v2 | Accuracy | 77.39 |
Domain Adaptation > Domain Generalization | NICO Vehicle | CORAL (Resnet-18) | http://arxiv.org/abs/1607.01719v1 | Accuracy | 71.64 |
Domain Adaptation > Domain Generalization | DomainNet | L2C (CLIP, ViT-L/14) | https://openreview.net/forum?id=TD3SGJfBC7 | Average Accuracy | 67.4 |
Domain Adaptation > Domain Generalization | DomainNet | PromptStyler (CLIP, ViT-L/14) | https://arxiv.org/abs/2307.15199v2 | Average Accuracy | 65.5 |
Domain Adaptation > Domain Generalization | DomainNet | VDPG (CLIP, ViT-L/14) | https://arxiv.org/abs/2405.02797v1 | Average Accuracy | 65.2 |
Domain Adaptation > Domain Generalization | DomainNet | VL2V-SD (CLIP, ViT-B/16) | https://arxiv.org/abs/2310.08255v2 | Average Accuracy | 62.79 |
Domain Adaptation > Domain Generalization | DomainNet | MoA (OpenCLIP, ViT-B/16) | https://arxiv.org/abs/2310.11031v2 | Average Accuracy | 62.7 |
Domain Adaptation > Domain Generalization | DomainNet | CAR-FT (CLIP, ViT-B/16) | https://arxiv.org/abs/2211.16175v1 | Average Accuracy | 62.5 |
Domain Adaptation > Domain Generalization | DomainNet | SIMPLE+ | https://openreview.net/forum?id=BqrPeZ_e5P | Average Accuracy | 61.9 |
Domain Adaptation > Domain Generalization | DomainNet | GMDG (RegNetY-16GF, SWAD) | https://arxiv.org/abs/2402.18853v2 | Average Accuracy | 61.3 |
Domain Adaptation > Domain Generalization | DomainNet | L2C (CLIP, ViT-B/16) | https://openreview.net/forum?id=TD3SGJfBC7 | Average Accuracy | 61.2 |
Domain Adaptation > Domain Generalization | DomainNet | Ensemble of Averages (RegNetY-16GF) | https://arxiv.org/abs/2110.10832v4 | Average Accuracy | 60.9 |
Domain Adaptation > Domain Generalization | DomainNet | MIRO (RegNetY-16GF, SWAD) | https://arxiv.org/abs/2203.10789v2 | Average Accuracy | 60.7 |
Domain Adaptation > Domain Generalization | DomainNet | SPG (CLIP, ViT-B/16) | https://arxiv.org/abs/2404.19286v2 | Average Accuracy | 60.1 |
Domain Adaptation > Domain Generalization | DomainNet | VDPG (CLIP, ViT-B/16) | https://arxiv.org/abs/2405.02797v1 | Average Accuracy | 59.8 |
Domain Adaptation > Domain Generalization | DomainNet | UniDG + CORAL + ConvNeXt-B | https://arxiv.org/abs/2310.10008v1 | Average Accuracy | 59.5 |
Domain Adaptation > Domain Generalization | DomainNet | PromptStyler (CLIP, ViT-B/16) | https://arxiv.org/abs/2307.15199v2 | Average Accuracy | 59.4 |
Domain Adaptation > Domain Generalization | DomainNet | SEDGE+ | https://arxiv.org/abs/2203.04600v1 | Average Accuracy | 54.7 |
Domain Adaptation > Domain Generalization | DomainNet | Ensemble of Averages (ResNeXt-50 32x4d) | https://arxiv.org/abs/2110.10832v4 | Average Accuracy | 54.6 |
Domain Adaptation > Domain Generalization | DomainNet | GMDG (RegNetY-16GF) | https://arxiv.org/abs/2402.18853v2 | Average Accuracy | 54.6 |
Domain Adaptation > Domain Generalization | DomainNet | Hybrid-SF-MoE | https://arxiv.org/abs/2206.04046v6 | Average Accuracy | 52.0 |
Domain Adaptation > Domain Generalization | DomainNet | CADG | https://arxiv.org/abs/2203.17067v3 | Average Accuracy | 51.6 |
Domain Adaptation > Domain Generalization | DomainNet | SPG (CLIP, ResNet-50) | https://arxiv.org/abs/2404.19286v2 | Average Accuracy | 50.1 |
Domain Adaptation > Domain Generalization | DomainNet | PromptStyler (CLIP, ResNet-50) | https://arxiv.org/abs/2307.15199v2 | Average Accuracy | 49.5 |
Domain Adaptation > Domain Generalization | DomainNet | SIMPLE | https://openreview.net/forum?id=BqrPeZ_e5P | Average Accuracy | 49.2 |
Domain Adaptation > Domain Generalization | DomainNet | GMoE-S/16 | https://arxiv.org/abs/2206.04046v6 | Average Accuracy | 48.7 |
Domain Adaptation > Domain Generalization | DomainNet | Ensemble of Averages (ResNet-50) | https://arxiv.org/abs/2110.10832v4 | Average Accuracy | 47.4 |
Domain Adaptation > Domain Generalization | DomainNet | GMDG (ResNet-50, SWAD) | https://arxiv.org/abs/2402.18853v2 | Average Accuracy | 47.3 |
Domain Adaptation > Domain Generalization | DomainNet | MIRO (ResNet-50, SWAD) | https://arxiv.org/abs/2203.10789v2 | Average Accuracy | 47.0 |
Domain Adaptation > Domain Generalization | DomainNet | DDG | https://arxiv.org/abs/2205.13913v1 | Average Accuracy | 46.93 |
Domain Adaptation > Domain Generalization | DomainNet | AdaClust (ResNet-50, SWAD) | https://arxiv.org/abs/2112.04766v2 | Average Accuracy | 46.7 |
Domain Adaptation > Domain Generalization | DomainNet | SWAD (ResNet-50) | https://arxiv.org/abs/2102.08604v4 | Average Accuracy | 46.5 |
Domain Adaptation > Domain Generalization | DomainNet | SEDGE | https://arxiv.org/abs/2203.04600v1 | Average Accuracy | 46.3 |
Domain Adaptation > Domain Generalization | DomainNet | GMDG (ResNet-50) | https://arxiv.org/abs/2402.18853v2 | Average Accuracy | 44.6 |
Domain Adaptation > Domain Generalization | DomainNet | Meta-DMoE (ResNet-50) | https://arxiv.org/abs/2210.03885v2 | Average Accuracy | 44.2 |
Domain Adaptation > Domain Generalization | DomainNet | POEM | https://arxiv.org/abs/2305.13046v1 | Average Accuracy | 44.0 |
Domain Adaptation > Domain Generalization | DomainNet | DMG (ResNet-50) | https://arxiv.org/abs/2008.12839v1 | Average Accuracy | 43.63 |
Domain Adaptation > Domain Generalization | DomainNet | MetaReg (ResNet-50) | https://arxiv.org/abs/2008.12839v1 | Average Accuracy | 43.62 |
Domain Adaptation > Domain Generalization | DomainNet | AdaClust (ResNet-50) | https://arxiv.org/abs/2112.04766v2 | Average Accuracy | 43.3 |
Domain Adaptation > Domain Generalization | DomainNet | Fishr (ResNet-50) | https://arxiv.org/abs/2109.02934v3 | Average Accuracy | 41.8 |
Domain Adaptation > Domain Generalization | GTA5-to-Cityscapes | tqdm (EVA02-CLIP-L) | https://arxiv.org/abs/2407.09033v1 | mIoU | 68.88 |
Domain Adaptation > Domain Generalization | GTA5-to-Cityscapes | ADSI | https://arxiv.org/abs/2504.06781v1 | mIoU | 67.75 |
Domain Adaptation > Domain Generalization | GTA5-to-Cityscapes | Rein | https://arxiv.org/abs/2312.04265v5 | mIoU | 66.4 |
Domain Adaptation > Domain Generalization | GTA5-to-Cityscapes | VLTSeg (EVA02-CLIP-L) | https://arxiv.org/abs/2312.02021v4 | mIoU | 65.6 |
Domain Adaptation > Domain Generalization | GTA5-to-Cityscapes | DIFF | https://arxiv.org/abs/2406.00777v2 | mIoU | 58.01 |
Domain Adaptation > Domain Generalization | GTA5-to-Cityscapes | CMFormer | https://arxiv.org/abs/2307.00371v5 | mIoU | 55.31 |
Domain Adaptation > Domain Generalization | GTA5-to-Cityscapes | Self-adaptation (ResNet - 101) | https://arxiv.org/abs/2208.05788v3 | mIoU | 46.99 |
Domain Adaptation > Domain Generalization | GTA5-to-Cityscapes | GtA-SFDA Source-Only (DeepLabv2-ResNet101) | https://arxiv.org/abs/2108.11249v1 | mIoU | 43.5 |
Domain Adaptation > Domain Generalization | ImageNet-C | DINOv2 (ViT-g/14, frozen model, linear eval) | https://arxiv.org/abs/2304.07193v2 | mean Corruption Error (mCE) | 28.2 |
Domain Adaptation > Domain Generalization | ImageNet-C | DINOv2 (ViT-g/14, frozen model, linear eval) | https://arxiv.org/abs/2304.07193v2 | Number of params | 1100M |
Domain Adaptation > Domain Generalization | ImageNet-C | CAFormer-B36 (IN21K, 384) | https://arxiv.org/abs/2210.13452v4 | mean Corruption Error (mCE) | 30.8 |
Domain Adaptation > Domain Generalization | ImageNet-C | CAFormer-B36 (IN21K, 384) | https://arxiv.org/abs/2210.13452v4 | Number of params | 99M |
Domain Adaptation > Domain Generalization | ImageNet-C | MAE+DAT (ViT-H) | https://arxiv.org/abs/2209.07735v1 | mean Corruption Error (mCE) | 31.4 |
Domain Adaptation > Domain Generalization | ImageNet-C | MAE+DAT (ViT-H) | https://arxiv.org/abs/2209.07735v1 | Number of params | 632M |
Domain Adaptation > Domain Generalization | ImageNet-C | DINOv2 (ViT-L/14, frozen model, linear eval) | https://arxiv.org/abs/2304.07193v2 | mean Corruption Error (mCE) | 31.5 |
Domain Adaptation > Domain Generalization | ImageNet-C | DINOv2 (ViT-L/14, frozen model, linear eval) | https://arxiv.org/abs/2304.07193v2 | Number of params | 307M |
Domain Adaptation > Domain Generalization | ImageNet-C | CAFormer-B36 (IN21K) | https://arxiv.org/abs/2210.13452v4 | mean Corruption Error (mCE) | 31.8 |
Domain Adaptation > Domain Generalization | ImageNet-C | MAE (ViT-H) | https://arxiv.org/abs/2111.06377v2 | mean Corruption Error (mCE) | 33.8 |
Domain Adaptation > Domain Generalization | ImageNet-C | MAE (ViT-H) | https://arxiv.org/abs/2111.06377v2 | Number of params | 632M |
Domain Adaptation > Domain Generalization | ImageNet-C | ConvFormer-B36 (IN21K) | https://arxiv.org/abs/2210.13452v4 | mean Corruption Error (mCE) | 35.0 |
Domain Adaptation > Domain Generalization | ImageNet-C | FAN-L-Hybrid (IN-22k) | https://arxiv.org/abs/2204.12451v4 | mean Corruption Error (mCE) | 35.8 |
Domain Adaptation > Domain Generalization | ImageNet-C | FAN-L-Hybrid (IN-22k) | https://arxiv.org/abs/2204.12451v4 | Top 1 Accuracy | 73.6 |
Domain Adaptation > Domain Generalization | ImageNet-C | FAN-L-Hybrid (IN-22k) | https://arxiv.org/abs/2204.12451v4 | Number of params | 77M |
Domain Adaptation > Domain Generalization | ImageNet-C | Pyramid Adversarial Training Improves ViT (Im21k) | https://arxiv.org/abs/2111.15121v2 | mean Corruption Error (mCE) | 36.80 |
Domain Adaptation > Domain Generalization | ImageNet-C | Pyramid Adversarial Training Improves ViT (Im21k) | https://arxiv.org/abs/2111.15121v2 | Number of params | 87M |
Domain Adaptation > Domain Generalization | ImageNet-C | VOLO-D5+HAT | https://arxiv.org/abs/2204.00993v3 | mean Corruption Error (mCE) | 38.4 |
Domain Adaptation > Domain Generalization | ImageNet-C | VOLO-D5+HAT | https://arxiv.org/abs/2204.00993v3 | Number of params | 296M |
Domain Adaptation > Domain Generalization | ImageNet-C | DiscreteViT (Im21k) | https://arxiv.org/abs/2111.10493v2 | mean Corruption Error (mCE) | 38.74 |
Domain Adaptation > Domain Generalization | ImageNet-C | DiscreteViT (Im21k) | https://arxiv.org/abs/2111.10493v2 | Number of params | 87M |
Domain Adaptation > Domain Generalization | ImageNet-C | ConvNeXt-XL (Im21k) (augmentation overlap with ImageNet-C) | https://arxiv.org/abs/2201.03545v2 | mean Corruption Error (mCE) | 38.8 |
Domain Adaptation > Domain Generalization | ImageNet-C | ConvNeXt-XL (Im21k) (augmentation overlap with ImageNet-C) | https://arxiv.org/abs/2201.03545v2 | Number of params | 350M |
Domain Adaptation > Domain Generalization | ImageNet-C | GPaCo (ViT-L) | https://arxiv.org/abs/2209.12400v2 | mean Corruption Error (mCE) | 39.0 |
Domain Adaptation > Domain Generalization | ImageNet-C | FAN-B-Hybrid (IN-22k) | https://arxiv.org/abs/2204.12451v4 | mean Corruption Error (mCE) | 41.0 |
Domain Adaptation > Domain Generalization | ImageNet-C | FAN-B-Hybrid (IN-22k) | https://arxiv.org/abs/2204.12451v4 | Top 1 Accuracy | 70.5 |
Domain Adaptation > Domain Generalization | ImageNet-C | FAN-B-Hybrid (IN-22k) | https://arxiv.org/abs/2204.12451v4 | Number of params | 50M |
Domain Adaptation > Domain Generalization | ImageNet-C | Pyramid Adversarial Training Improves ViT | https://arxiv.org/abs/2111.15121v2 | mean Corruption Error (mCE) | 41.42 |
Domain Adaptation > Domain Generalization | ImageNet-C | FAN-L-Hybrid+STL | https://arxiv.org/abs/2401.03844v1 | mean Corruption Error (mCE) | 42.1 |
Domain Adaptation > Domain Generalization | ImageNet-C | FAN-L-Hybrid+STL | https://arxiv.org/abs/2401.03844v1 | Top 1 Accuracy | 69.2 |
Domain Adaptation > Domain Generalization | ImageNet-C | FAN-L-Hybrid+STL | https://arxiv.org/abs/2401.03844v1 | Number of params | 77M |
Domain Adaptation > Domain Generalization | ImageNet-C | QualNet (ResNeXt101) | http://openaccess.thecvf.com//content/CVPR2021/html/Kim_Quality-Agnostic_Image_Recognition_via_Invertible_Decoder_CVPR_2021_paper.html | mean Corruption Error (mCE) | 42.5 |
Domain Adaptation > Domain Generalization | ImageNet-C | CAFormer-B36 | https://arxiv.org/abs/2210.13452v4 | mean Corruption Error (mCE) | 42.6 |
Domain Adaptation > Domain Generalization | ImageNet-C | DINOv2 (ViT-B/14, frozen model, linear eval) | https://arxiv.org/abs/2304.07193v2 | mean Corruption Error (mCE) | 42.7 |
Domain Adaptation > Domain Generalization | ImageNet-C | DINOv2 (ViT-B/14, frozen model, linear eval) | https://arxiv.org/abs/2304.07193v2 | Number of params | 85M |
Domain Adaptation > Domain Generalization | ImageNet-C | FAN-L-Hybrid | https://arxiv.org/abs/2204.12451v4 | mean Corruption Error (mCE) | 43.0 |
Domain Adaptation > Domain Generalization | ImageNet-C | FAN-L-Hybrid | https://arxiv.org/abs/2204.12451v4 | Top 1 Accuracy | 67.7 |
Domain Adaptation > Domain Generalization | ImageNet-C | FAN-L-Hybrid | https://arxiv.org/abs/2204.12451v4 | Number of params | 77M |
Domain Adaptation > Domain Generalization | ImageNet-C | DrViT | https://arxiv.org/abs/2111.10493v2 | mean Corruption Error (mCE) | 46.22 |
Domain Adaptation > Domain Generalization | ImageNet-C | DiscreteViT | https://arxiv.org/abs/2111.10493v2 | mean Corruption Error (mCE) | 46.22 |
Domain Adaptation > Domain Generalization | ImageNet-C | DiscreteViT | https://arxiv.org/abs/2111.10493v2 | Number of params | 87M |
Domain Adaptation > Domain Generalization | ImageNet-C | ConvFormer-B36 | https://arxiv.org/abs/2210.13452v4 | mean Corruption Error (mCE) | 46.3 |
Domain Adaptation > Domain Generalization | ImageNet-C | RVT-B* | https://arxiv.org/abs/2105.07926v4 | mean Corruption Error (mCE) | 46.8 |
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