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 | PACS | CSD (Resnet-18) | https://arxiv.org/abs/2003.12815v2 | Average Accuracy | 80.69 |
Domain Adaptation > Domain Generalization | PACS | JiGen (Resnet-18) | http://arxiv.org/abs/1903.06864v2 | Average Accuracy | 80.51 |
Domain Adaptation > Domain Generalization | PACS | DADG (Resnet-18) | https://arxiv.org/abs/2011.00444v2 | Average Accuracy | 80.38 |
Domain Adaptation > Domain Generalization | PACS | CCSA (Resnet-18) | http://arxiv.org/abs/1709.10190v1 | Average Accuracy | 79.4 |
Domain Adaptation > Domain Generalization | PACS | DDEC (Alexnet) | https://arxiv.org/abs/1909.08245v2 | Average Accuracy | 79.15 |
Domain Adaptation > Domain Generalization | PACS | Deep All (Resnet-18) | http://arxiv.org/abs/1903.06864v2 | Average Accuracy | 79.05 |
Domain Adaptation > Domain Generalization | PACS | Stylized (Alexnet) | https://arxiv.org/abs/2101.09060v2 | Average Accuracy | 77.31 |
Domain Adaptation > Domain Generalization | PACS | MMD-AAE (Resnet-18) | http://openaccess.thecvf.com/content_cvpr_2018/html/Li_Domain_Generalization_With_CVPR_2018_paper.html | Average Accuracy | 77.0 |
Domain Adaptation > Domain Generalization | PACS | DGIS (Alexnet, Painting) | https://arxiv.org/abs/2006.11207v2 | Average Accuracy | 76.62 |
Domain Adaptation > Domain Generalization | PACS | RSC (AlexNet) | https://arxiv.org/abs/2007.02454v1 | Average Accuracy | 76.05 |
Domain Adaptation > Domain Generalization | PACS | ER (Alexnet) | http://proceedings.neurips.cc/paper/2020/hash/b98249b38337c5088bbc660d8f872d6a-Abstract.html | Average Accuracy | 75.67 |
Domain Adaptation > Domain Generalization | PACS | SagNet (Alexnet) | https://arxiv.org/abs/1910.11645v4 | Average Accuracy | 75.52 |
Domain Adaptation > Domain Generalization | PACS | MASF (Alexnet) | https://arxiv.org/abs/1910.13580v1 | Average Accuracy | 75.21 |
Domain Adaptation > Domain Generalization | PACS | MMLD (Alexnet, k=3) | https://arxiv.org/abs/1911.07661v1 | Average Accuracy | 74.38 |
Domain Adaptation > Domain Generalization | PACS | Jigsaw+Rotation (Alexnet) | https://arxiv.org/abs/2007.12368v2 | Average Accuracy | 74.08 |
Domain Adaptation > Domain Generalization | PACS | G2DM (AlexNet) | https://arxiv.org/abs/1911.00804v6 | Average Accuracy | 73.55 |
Domain Adaptation > Domain Generalization | PACS | GLOT-DR | https://arxiv.org/abs/2203.00553v3 | Average Accuracy | 73.5 |
Domain Adaptation > Domain Generalization | PACS | JiGen (Alexnet) | http://arxiv.org/abs/1903.06864v2 | Average Accuracy | 73.38 |
Domain Adaptation > Domain Generalization | PACS | DMG (Alexnet) | https://arxiv.org/abs/2008.12839v1 | Average Accuracy | 73.32 |
Domain Adaptation > Domain Generalization | PACS | MetaReg (Alexnet) | http://papers.nips.cc/paper/7378-metareg-towards-domain-generalization-using-meta-regularization | Average Accuracy | 72.62 |
Domain Adaptation > Domain Generalization | PACS | DADG (AlexNet) | https://arxiv.org/abs/2011.00444v2 | Average Accuracy | 72.11 |
Domain Adaptation > Domain Generalization | PACS | PAR (Alexnet) | https://arxiv.org/abs/1905.13549v2 | Average Accuracy | 72.08 |
Domain Adaptation > Domain Generalization | PACS | Epi-FCR (Alexnet) | https://arxiv.org/abs/1902.00113v3 | Average Accuracy | 72.00 |
Domain Adaptation > Domain Generalization | PACS | CAADG (Alexnet) | https://arxiv.org/abs/1911.12983v1 | Average Accuracy | 71.98 |
Domain Adaptation > Domain Generalization | PACS | Deep All (Alexnet) | http://arxiv.org/abs/1903.06864v2 | Average Accuracy | 71.52 |
Domain Adaptation > Domain Generalization | PACS | D-SAM (Alexnet) | http://arxiv.org/abs/1809.10966v1 | Average Accuracy | 71.20 |
Domain Adaptation > Domain Generalization | PACS | VREx (Alexnet) | https://arxiv.org/abs/2003.00688v5 | Average Accuracy | 71.14 |
Domain Adaptation > Domain Generalization | PACS | RC (Alexnet) | https://arxiv.org/abs/2007.13003v3 | Average Accuracy | 70.53 |
Domain Adaptation > Domain Generalization | PACS | FeatureCritic (Alexnet) | https://arxiv.org/abs/1901.11448v3 | Average Accuracy | 70.40 |
Domain Adaptation > Domain Generalization | PACS | BestSources (Alexnet) | http://arxiv.org/abs/1806.05810v1 | Average Accuracy | 70.30 |
Domain Adaptation > Domain Generalization | PACS | Hex (Alexnet) | http://arxiv.org/abs/1903.06256v1 | Average Accuracy | 70.20 |
Domain Adaptation > Domain Generalization | PACS | MLDG (Alexnet) | http://arxiv.org/abs/1710.03463v1 | Average Accuracy | 70.01 |
Domain Adaptation > Domain Generalization | PACS | JAN-COMBO (Alexnet) | http://arxiv.org/abs/1812.08974v1 | Average Accuracy | 69.45 |
Domain Adaptation > Domain Generalization | PACS | Rotation+Gabor+DeepCluster (Alexnet) | https://arxiv.org/abs/2003.13525v1 | Average Accuracy | 69.32 |
Domain Adaptation > Domain Generalization | PACS | TF (Alexnet) | http://arxiv.org/abs/1710.03077v1 | Average Accuracy | 69.21 |
Domain Adaptation > Domain Generalization | PACS | CIDDG (Alexnet) | http://openaccess.thecvf.com/content_ECCV_2018/html/Ya_Li_Deep_Domain_Generalization_ECCV_2018_paper.html | Average Accuracy | 68.88 |
Domain Adaptation > Domain Generalization | PACS | DSN (Alexnet) | http://arxiv.org/abs/1608.06019v1 | Average Accuracy | 67.37 |
Domain Adaptation > Domain Generalization | PACS | LRE-SVM | http://arxiv.org/abs/1710.03077v1 | Average Accuracy | 58.99 |
Domain Adaptation > Domain Generalization | PACS | SVM | http://arxiv.org/abs/1710.03077v1 | Average Accuracy | 58.74 |
Domain Adaptation > Domain Generalization | PACS | SGE (GNN-Tag) | https://arxiv.org/abs/2109.05671v1 | Average Accuracy | 47.45 |
Domain Adaptation > Domain Generalization | PACS | BayesPrompt | null | Average Accuracy | 0 |
Domain Adaptation > Domain Generalization | Stylized-ImageNet | MAE+DAT (ViT-H) | https://arxiv.org/abs/2209.07735v1 | Top 1 Accuracy | 32.77 |
Domain Adaptation > Domain Generalization | Stylized-ImageNet | VOLO-D5+HAT | https://arxiv.org/abs/2204.00993v3 | Top 1 Accuracy | 25.9 |
Domain Adaptation > Domain Generalization | Stylized-ImageNet | DiscreteViT | https://arxiv.org/abs/2111.10493v2 | Top 1 Accuracy | 22.19 |
Domain Adaptation > Domain Generalization | CIFAR-100C | GLOT-DR | https://arxiv.org/abs/2203.00553v3 | Accuracy | 58.4 |
Domain Adaptation > Domain Generalization | GTA-to-Avg(Cityscapes,BDD,Mapillary) | SoRA | https://arxiv.org/abs/2412.04077v1 | mIoU | 68.27 |
Domain Adaptation > Domain Generalization | GTA-to-Avg(Cityscapes,BDD,Mapillary) | MFuser | https://arxiv.org/abs/2504.03193v1 | mIoU | 68.20 |
Domain Adaptation > Domain Generalization | GTA-to-Avg(Cityscapes,BDD,Mapillary) | tqdm (EVA02-CLIP-L) | https://arxiv.org/abs/2407.09033v1 | mIoU | 66.05 |
Domain Adaptation > Domain Generalization | GTA-to-Avg(Cityscapes,BDD,Mapillary) | ADSI | https://arxiv.org/abs/2504.06781v1 | mIoU | 65.57 |
Domain Adaptation > Domain Generalization | GTA-to-Avg(Cityscapes,BDD,Mapillary) | Rein | https://arxiv.org/abs/2312.04265v5 | mIoU | 64.3 |
Domain Adaptation > Domain Generalization | GTA-to-Avg(Cityscapes,BDD,Mapillary) | VLTSeg | https://arxiv.org/abs/2312.02021v4 | mIoU | 63.5 |
Domain Adaptation > Domain Generalization | GTA-to-Avg(Cityscapes,BDD,Mapillary) | CLOUDS | https://arxiv.org/abs/2312.09788v2 | mIoU | 61.5 |
Domain Adaptation > Domain Generalization | GTA-to-Avg(Cityscapes,BDD,Mapillary) | DIDEX | https://arxiv.org/abs/2312.01850v1 | mIoU | 59.7 |
Domain Adaptation > Domain Generalization | GTA-to-Avg(Cityscapes,BDD,Mapillary) | DGInStyle | https://arxiv.org/abs/2312.03048v2 | mIoU | 57.78 |
Domain Adaptation > Domain Generalization | GTA-to-Avg(Cityscapes,BDD,Mapillary) | DIFF | https://arxiv.org/abs/2406.00777v2 | mIoU | 57.15 |
Domain Adaptation > Domain Generalization | GTA-to-Avg(Cityscapes,BDD,Mapillary) | HRDA | https://arxiv.org/abs/2304.13615v2 | mIoU | 55.9 |
Domain Adaptation > Domain Generalization | GTA-to-Avg(Cityscapes,BDD,Mapillary) | DAFormer | https://arxiv.org/abs/2304.13615v2 | mIoU | 51.73 |
Domain Adaptation > Domain Generalization | GTA-to-Avg(Cityscapes,BDD,Mapillary) | CMFormer | https://arxiv.org/abs/2307.00371v5 | mIoU | 51.10 |
Domain Adaptation > Domain Generalization | GTA-to-Avg(Cityscapes,BDD,Mapillary) | ReVT | https://arxiv.org/abs/2308.13331v1 | mIoU | 50.2 |
Domain Adaptation > Domain Generalization | GTA-to-Avg(Cityscapes,BDD,Mapillary) | TLDR | https://arxiv.org/abs/2303.11546v2 | mIoU | 47.09 |
Domain Adaptation > Domain Generalization | GTA-to-Avg(Cityscapes,BDD,Mapillary) | SHADE | https://arxiv.org/abs/2204.02548v2 | mIoU | 45.27 |
Domain Adaptation > Domain Generalization | GTA-to-Avg(Cityscapes,BDD,Mapillary) | SAN-SAW | https://arxiv.org/abs/2204.00822v1 | mIoU | 42.43 |
Domain Adaptation > Domain Generalization | GTA-to-Avg(Cityscapes,BDD,Mapillary) | AdvStyle | https://arxiv.org/abs/2207.04892v2 | mIoU | 42.42 |
Domain Adaptation > Domain Generalization | GTA-to-Avg(Cityscapes,BDD,Mapillary) | GTR | https://arxiv.org/abs/2108.02376v2 | mIoU | 40.8 |
Domain Adaptation > Domain Generalization | GTA-to-Avg(Cityscapes,BDD,Mapillary) | DRPC | https://arxiv.org/abs/1909.00889v2 | mIoU | 39.77 |
Domain Adaptation > Domain Generalization | GTA-to-Avg(Cityscapes,BDD,Mapillary) | RobustNet | https://arxiv.org/abs/2103.15597v2 | mIoU | 37.37 |
Domain Adaptation > Domain Generalization | GTA-to-Avg(Cityscapes,BDD,Mapillary) | IBN | https://arxiv.org/abs/1807.09441v3 | mIoU | 34.63 |
Domain Adaptation > Domain Generalization | GTA-to-Avg(Cityscapes,BDD,Mapillary) | Self-adaptation (ResNet - 50) | https://arxiv.org/abs/2208.05788v3 | mIoU | 44,07 |
Domain Adaptation > Domain Generalization | GTA-to-Avg(Cityscapes,BDD,Mapillary) | Self-adaptation (ResNet - 101) | https://arxiv.org/abs/2208.05788v3 | mIoU | 44,89 |
Domain Adaptation > Domain Generalization | ImageNet-R | Model soups (BASIC-L) | https://arxiv.org/abs/2203.05482v3 | Top-1 Error Rate | 3.90 |
Domain Adaptation > Domain Generalization | ImageNet-R | Model soups (ViT-G/14) | https://arxiv.org/abs/2203.05482v3 | Top-1 Error Rate | 4.54 |
Domain Adaptation > Domain Generalization | ImageNet-R | CAR-FT (CLIP, ViT-L/14@336px) | https://arxiv.org/abs/2211.16175v1 | Top-1 Error Rate | 10.3 |
Domain Adaptation > Domain Generalization | ImageNet-R | FAN-Hybrid-L(IN-21K, 384)) | https://arxiv.org/abs/2204.12451v4 | Top-1 Error Rate | 28.9 |
Domain Adaptation > Domain Generalization | ImageNet-R | CAFormer-B36 (IN21K, 384) | https://arxiv.org/abs/2210.13452v4 | Top-1 Error Rate | 29.6 |
Domain Adaptation > Domain Generalization | ImageNet-R | LLE (ViT-B/16, SWAG, Edge Aug) | https://arxiv.org/abs/2212.04825v2 | Top-1 Error Rate | 31.3 |
Domain Adaptation > Domain Generalization | ImageNet-R | CAFormer-B36 (IN21K) | https://arxiv.org/abs/2210.13452v4 | Top-1 Error Rate | 31.7 |
Domain Adaptation > Domain Generalization | ImageNet-R | ConvNeXt-XL (Im21k, 384) | https://arxiv.org/abs/2201.03545v2 | Top-1 Error Rate | 31.8 |
Domain Adaptation > Domain Generalization | ImageNet-R | LLE (ViT-H/14, MAE, Edge Aug) | https://arxiv.org/abs/2212.04825v2 | Top-1 Error Rate | 33.1 |
Domain Adaptation > Domain Generalization | ImageNet-R | MAE (ViT-H, 448) | https://arxiv.org/abs/2111.06377v2 | Top-1 Error Rate | 33.5 |
Domain Adaptation > Domain Generalization | ImageNet-R | ConvFormer-B36 (IN21K, 384) | https://arxiv.org/abs/2210.13452v4 | Top-1 Error Rate | 33.5 |
Domain Adaptation > Domain Generalization | ImageNet-R | MAE+DAT (ViT-H) | https://arxiv.org/abs/2209.07735v1 | Top-1 Error Rate | 34.39 |
Domain Adaptation > Domain Generalization | ImageNet-R | ConvFormer-B36 (IN21K) | https://arxiv.org/abs/2210.13452v4 | Top-1 Error Rate | 34.7 |
Domain Adaptation > Domain Generalization | ImageNet-R | Discrete Adversarial Distillation (ViT-B,224) | https://arxiv.org/abs/2311.01441v2 | Top-1 Error Rate | 34.9 |
Domain Adaptation > Domain Generalization | ImageNet-R | GPaCo (ViT-L) | https://arxiv.org/abs/2209.12400v2 | Top-1 Error Rate | 39.7 |
Domain Adaptation > Domain Generalization | ImageNet-R | VOLO-D5+HAT | https://arxiv.org/abs/2204.00993v3 | Top-1 Error Rate | 40.3 |
Domain Adaptation > Domain Generalization | ImageNet-R | Pyramid Adversarial Training Improves ViT (Im21k) | https://arxiv.org/abs/2111.15121v2 | Top-1 Error Rate | 42.16 |
Domain Adaptation > Domain Generalization | ImageNet-R | FAN-L-Hybrid+STL | https://arxiv.org/abs/2401.03844v1 | Top-1 Error Rate | 43.4 |
Domain Adaptation > Domain Generalization | ImageNet-R | SEER (RegNet10B) | https://arxiv.org/abs/2202.08360v2 | Top-1 Error Rate | 43.9 |
Domain Adaptation > Domain Generalization | ImageNet-R | DiscreteViT | https://arxiv.org/abs/2111.10493v2 | Top-1 Error Rate | 44.74 |
Domain Adaptation > Domain Generalization | ImageNet-R | CAFormer-B36 (384) | https://arxiv.org/abs/2210.13452v4 | Top-1 Error Rate | 45 |
Domain Adaptation > Domain Generalization | ImageNet-R | Pyramid Adversarial Training Improves ViT | https://arxiv.org/abs/2111.15121v2 | Top-1 Error Rate | 46.08 |
Domain Adaptation > Domain Generalization | ImageNet-R | CAFormer-B36 | https://arxiv.org/abs/2210.13452v4 | Top-1 Error Rate | 46.1 |
Domain Adaptation > Domain Generalization | ImageNet-R | ConvFormer-B36 (384) | https://arxiv.org/abs/2210.13452v4 | Top-1 Error Rate | 47.8 |
Domain Adaptation > Domain Generalization | ImageNet-R | ConvFormer-B36 | https://arxiv.org/abs/2210.13452v4 | Top-1 Error Rate | 48.9 |
Domain Adaptation > Domain Generalization | ImageNet-R | RVT-B* | https://arxiv.org/abs/2105.07926v4 | Top-1 Error Rate | 51.3 |
Domain Adaptation > Domain Generalization | ImageNet-R | Sequencer2D-L | https://arxiv.org/abs/2205.01972v4 | Top-1 Error Rate | 51.9 |
Domain Adaptation > Domain Generalization | ImageNet-R | RVT-S* | https://arxiv.org/abs/2105.07926v4 | Top-1 Error Rate | 52.3 |
Domain Adaptation > Domain Generalization | ImageNet-R | DeepAugment+AugMix (ResNet-50) | https://arxiv.org/abs/2006.16241v3 | Top-1 Error Rate | 53.2 |
Domain Adaptation > Domain Generalization | ImageNet-R | PRIME with JSD (ResNet-50) | https://arxiv.org/abs/2112.13547v2 | Top-1 Error Rate | 53.7 |
Domain Adaptation > Domain Generalization | ImageNet-R | RVT-Ti* | https://arxiv.org/abs/2105.07926v4 | Top-1 Error Rate | 56.1 |
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