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-R | PRIME (ResNet-50) | https://arxiv.org/abs/2112.13547v2 | Top-1 Error Rate | 57.1 |
Domain Adaptation > Domain Generalization | ImageNet-R | DeepAugment (ResNet-50) | https://arxiv.org/abs/2006.16241v3 | Top-1 Error Rate | 57.8 |
Domain Adaptation > Domain Generalization | ImageNet-R | Stylized ImageNet (ResNet-50) | https://arxiv.org/abs/1811.12231v3 | Top-1 Error Rate | 58.5 |
Domain Adaptation > Domain Generalization | ImageNet-R | AugMix (ResNet-50) | https://arxiv.org/abs/1912.02781v2 | Top-1 Error Rate | 58.9 |
Domain Adaptation > Domain Generalization | ImageNet-R | ResNet-50 | http://arxiv.org/abs/1512.03385v1 | Top-1 Error Rate | 63.9 |
Domain Adaptation > Domain Generalization | ImageNet-R | ResNet-152x2-SAM | https://arxiv.org/abs/2106.01548v3 | Top-1 Error Rate | 71.9 |
Domain Adaptation > Domain Generalization | ImageNet-R | ViT-B/16-SAM | https://arxiv.org/abs/2106.01548v3 | Top-1 Error Rate | 73.6 |
Domain Adaptation > Domain Generalization | ImageNet-R | Mixer-B/8-SAM | https://arxiv.org/abs/2106.01548v3 | Top-1 Error Rate | 76.5 |
Domain Adaptation > Domain Generalization | LipitK | CSD (Ours) | https://arxiv.org/abs/2003.12815v2 | Accuracy | 87.3 |
Domain Adaptation > Domain Generalization | Office-Home | MoA (OpenCLIP, ViT-B/16) | https://arxiv.org/abs/2310.11031v2 | Average Accuracy | 90.6 |
Domain Adaptation > Domain Generalization | Office-Home | PromptStyler (CLIP, ViT-L/14) | https://arxiv.org/abs/2307.15199v2 | Average Accuracy | 89.1 |
Domain Adaptation > Domain Generalization | Office-Home | UniDG + CORAL + ConvNeXt-B | https://arxiv.org/abs/2310.10008v1 | Average Accuracy | 88.9 |
Domain Adaptation > Domain Generalization | Office-Home | SIMPLE+ | https://openreview.net/forum?id=BqrPeZ_e5P | Average Accuracy | 87.7 |
Domain Adaptation > Domain Generalization | Office-Home | VL2V-SD (CLIP, ViT-B/16) | https://arxiv.org/abs/2310.08255v2 | Average Accuracy | 87.38 |
Domain Adaptation > Domain Generalization | Office-Home | CAR-FT (CLIP, ViT-B/16) | https://arxiv.org/abs/2211.16175v1 | Average Accuracy | 85.7 |
Domain Adaptation > Domain Generalization | Office-Home | GMDG (RegNetY-16GF, SWAD) | https://arxiv.org/abs/2402.18853v2 | Average Accuracy | 84.7 |
Domain Adaptation > Domain Generalization | Office-Home | SIMPLE | https://openreview.net/forum?id=BqrPeZ_e5P | Average Accuracy | 84.6 |
Domain Adaptation > Domain Generalization | Office-Home | Ensemble of Averages (RegNetY-16GF) | https://arxiv.org/abs/2110.10832v4 | Average Accuracy | 83.9 |
Domain Adaptation > Domain Generalization | Office-Home | PromptStyler (CLIP, ViT-B/16) | https://arxiv.org/abs/2307.15199v2 | Average Accuracy | 83.6 |
Domain Adaptation > Domain Generalization | Office-Home | SPG (CLIP, ViT-B/16) | https://arxiv.org/abs/2404.19286v2 | Average Accuracy | 83.6 |
Domain Adaptation > Domain Generalization | Office-Home | MIRO (RegNetY-16GF, SWAD) | https://arxiv.org/abs/2203.10789v2 | Average Accuracy | 83.3 |
Domain Adaptation > Domain Generalization | Office-Home | D-Triplet(RegNetY-16GF) | https://arxiv.org/abs/2303.01233v1 | Average Accuracy | 82.6 |
Domain Adaptation > Domain Generalization | Office-Home | GMDG (RegNetY-16GF) | https://arxiv.org/abs/2402.18853v2 | Average Accuracy | 80.8 |
Domain Adaptation > Domain Generalization | Office-Home | SEDGE+ | https://arxiv.org/abs/2203.04600v1 | Average Accuracy | 80.7 |
Domain Adaptation > Domain Generalization | Office-Home | Ensemble of Averages (ResNeXt-50 32x4d) | https://arxiv.org/abs/2110.10832v4 | Average Accuracy | 80.2 |
Domain Adaptation > Domain Generalization | Office-Home | SEDGE | https://arxiv.org/abs/2203.04600v1 | Average Accuracy | 79.9 |
Domain Adaptation > Domain Generalization | Office-Home | CADG | https://arxiv.org/abs/2203.17067v3 | Average Accuracy | 79.9 |
Domain Adaptation > Domain Generalization | Office-Home | GMoE-S/16 | https://arxiv.org/abs/2206.04046v6 | Average Accuracy | 74.2 |
Domain Adaptation > Domain Generalization | Office-Home | SPG (CLIP, ResNet-50) | https://arxiv.org/abs/2404.19286v2 | Average Accuracy | 73.8 |
Domain Adaptation > Domain Generalization | Office-Home | PromptStyler (CLIP, ResNet-50) | https://arxiv.org/abs/2307.15199v2 | Average Accuracy | 73.6 |
Domain Adaptation > Domain Generalization | Office-Home | Model Ratatouille | https://arxiv.org/abs/2212.10445v3 | Average Accuracy | 73.5 |
Domain Adaptation > Domain Generalization | Office-Home | Ensemble of Averages (ResNet-50) | https://arxiv.org/abs/2110.10832v4 | Average Accuracy | 72.5 |
Domain Adaptation > Domain Generalization | Office-Home | GMDG (ResNet-50, SWAD) | https://arxiv.org/abs/2402.18853v2 | Average Accuracy | 72.5 |
Domain Adaptation > Domain Generalization | Office-Home | MIRO (ResNet-50, SWAD) | https://arxiv.org/abs/2203.10789v2 | Average Accuracy | 72.4 |
Domain Adaptation > Domain Generalization | Office-Home | DDG | https://arxiv.org/abs/2205.13913v1 | Average Accuracy | 72.31 |
Domain Adaptation > Domain Generalization | Office-Home | PCL (swad+resnet50) | http://openaccess.thecvf.com//content/CVPR2022/html/Yao_PCL_Proxy-Based_Contrastive_Learning_for_Domain_Generalization_CVPR_2022_paper.html | Average Accuracy | 71.6 |
Domain Adaptation > Domain Generalization | Office-Home | VNE (ResNet-50, SWAD) | https://arxiv.org/abs/2304.01434v1 | Average Accuracy | 71.1 |
Domain Adaptation > Domain Generalization | Office-Home | GMDG (ResNet-50) | https://arxiv.org/abs/2402.18853v2 | Average Accuracy | 70.7 |
Domain Adaptation > Domain Generalization | Office-Home | SWAD (ResNet-50) | https://arxiv.org/abs/2102.08604v4 | Average Accuracy | 70.6 |
Domain Adaptation > Domain Generalization | Office-Home | WAKD (DeiT-Ti) | https://arxiv.org/abs/2309.11446v1 | Average Accuracy | 70.5 |
Domain Adaptation > Domain Generalization | Office-Home | D-Triplet(Resnet-50) | https://arxiv.org/abs/2303.01233v1 | Average Accuracy | 70.3 |
Domain Adaptation > Domain Generalization | Office-Home | DREAME | https://arxiv.org/abs/2112.09802v3 | Average Accuracy | 69.76 |
Domain Adaptation > Domain Generalization | Office-Home | AdaClust (ResNet-50, SWAD) | https://arxiv.org/abs/2112.04766v2 | Average Accuracy | 69.4 |
Domain Adaptation > Domain Generalization | Office-Home | Fishr (ResNet-50) | https://arxiv.org/abs/2109.02934v3 | Average Accuracy | 68.2 |
Domain Adaptation > Domain Generalization | Office-Home | POEM | https://arxiv.org/abs/2305.13046v1 | Average Accuracy | 68.0 |
Domain Adaptation > Domain Generalization | Office-Home | AdaClust (ResNet-50) | https://arxiv.org/abs/2112.04766v2 | Average Accuracy | 67.7 |
Domain Adaptation > Domain Generalization | Office-Home | XDED (ResNet-18) | https://arxiv.org/abs/2211.14058v1 | Average Accuracy | 67.4 |
Domain Adaptation > Domain Generalization | Office-Home | XDED (ResNet-18) | https://openreview.net/forum?id=63PjP_UEKe | Average Accuracy | 67.4 |
Domain Adaptation > Domain Generalization | Office-Home | WAKD (Resnet-18) | https://arxiv.org/abs/2309.11446v1 | Average Accuracy | 66.7 |
Domain Adaptation > Domain Generalization | Office-Home | Jone et al. (ResNet-50) | https://arxiv.org/abs/2108.08596v1 | Average Accuracy | 66.2 |
Domain Adaptation > Domain Generalization | Office-Home | LRDG (ResNet-18) | https://arxiv.org/abs/2212.07101v1 | Average Accuracy | 65.75 |
Domain Adaptation > Domain Generalization | Office-Home | RSC (ResNet18) | https://arxiv.org/abs/2007.02454v1 | Average Accuracy | 63.12 |
Domain Adaptation > Domain Generalization | Office-Home | SagNet (ResNet-18) | https://arxiv.org/abs/1910.11645v4 | Average Accuracy | 62.34 |
Domain Adaptation > Domain Generalization | Office-Home | DADG (ResNet-18) | https://arxiv.org/abs/2011.00444v2 | Average Accuracy | 62.22 |
Domain Adaptation > Domain Generalization | CIFAR-10C | GLOT-DR | https://arxiv.org/abs/2203.00553v3 | Accuracy | 84.5 |
Domain Adaptation > Domain Generalization | ImageNet-A | Model soups (BASIC-L) | https://arxiv.org/abs/2203.05482v3 | Top-1 accuracy % | 94.17 |
Domain Adaptation > Domain Generalization | ImageNet-A | Model soups (ViT-G/14) | https://arxiv.org/abs/2203.05482v3 | Top-1 accuracy % | 92.67 |
Domain Adaptation > Domain Generalization | ImageNet-A | µ2Net+ (ViT-L/16) | https://arxiv.org/abs/2209.07326v3 | Top-1 accuracy % | 84.53 |
Domain Adaptation > Domain Generalization | ImageNet-A | CAR-FT (CLIP, ViT-L/14@336px) | https://arxiv.org/abs/2211.16175v1 | Top-1 accuracy % | 81.5 |
Domain Adaptation > Domain Generalization | ImageNet-A | CAFormer-B36 (IN-21K, 384) | https://arxiv.org/abs/2210.13452v4 | Top-1 accuracy % | 79.5 |
Domain Adaptation > Domain Generalization | ImageNet-A | CAFormer-B36 (IN-21K, 384) | https://arxiv.org/abs/2210.13452v4 | Number of params | 99M |
Domain Adaptation > Domain Generalization | ImageNet-A | MAE (ViT-H, 448) | https://arxiv.org/abs/2111.06377v2 | Top-1 accuracy % | 76.7 |
Domain Adaptation > Domain Generalization | ImageNet-A | FAN-Hybrid-L(IN-21K, 384) | https://arxiv.org/abs/2204.12451v4 | Top-1 accuracy % | 74.5 |
Domain Adaptation > Domain Generalization | ImageNet-A | ConvFormer-B36 (IN-21K, 384) | https://arxiv.org/abs/2210.13452v4 | Top-1 accuracy % | 73.5 |
Domain Adaptation > Domain Generalization | ImageNet-A | ConvFormer-B36 (IN-21K, 384) | https://arxiv.org/abs/2210.13452v4 | Number of params | 100M |
Domain Adaptation > Domain Generalization | ImageNet-A | CAFormer-B36 (IN-21K) | https://arxiv.org/abs/2210.13452v4 | Top-1 accuracy % | 69.4 |
Domain Adaptation > Domain Generalization | ImageNet-A | CAFormer-B36 (IN-21K) | https://arxiv.org/abs/2210.13452v4 | Number of params | 99M |
Domain Adaptation > Domain Generalization | ImageNet-A | ConvNeXt-XL (Im21k, 384) | https://arxiv.org/abs/2201.03545v2 | Top-1 accuracy % | 69.3 |
Domain Adaptation > Domain Generalization | ImageNet-A | MAE+DAT (ViT-H) | https://arxiv.org/abs/2209.07735v1 | Top-1 accuracy % | 68.92 |
Domain Adaptation > Domain Generalization | ImageNet-A | ConvFormer-B36 (IN-21K) | https://arxiv.org/abs/2210.13452v4 | Top-1 accuracy % | 63.3 |
Domain Adaptation > Domain Generalization | ImageNet-A | ConvFormer-B36 (IN-21K) | https://arxiv.org/abs/2210.13452v4 | Number of params | 100M |
Domain Adaptation > Domain Generalization | ImageNet-A | Pyramid Adversarial Training Improves ViT (Im21k) | https://arxiv.org/abs/2111.15121v2 | Top-1 accuracy % | 62.44 |
Domain Adaptation > Domain Generalization | ImageNet-A | CAFormer-B36 (384) | https://arxiv.org/abs/2210.13452v4 | Top-1 accuracy % | 61.9 |
Domain Adaptation > Domain Generalization | ImageNet-A | CAFormer-B36 (384) | https://arxiv.org/abs/2210.13452v4 | Number of params | 99M |
Domain Adaptation > Domain Generalization | ImageNet-A | TransNeXt-Base (IN-1K supervised, 384) | https://arxiv.org/abs/2311.17132v3 | Top-1 accuracy % | 61.6 |
Domain Adaptation > Domain Generalization | ImageNet-A | TransNeXt-Base (IN-1K supervised, 384) | https://arxiv.org/abs/2311.17132v3 | Number of params | 89.7M |
Domain Adaptation > Domain Generalization | ImageNet-A | TransNeXt-Small (IN-1K supervised, 384) | https://arxiv.org/abs/2311.17132v3 | Top-1 accuracy % | 58.3 |
Domain Adaptation > Domain Generalization | ImageNet-A | TransNeXt-Small (IN-1K supervised, 384) | https://arxiv.org/abs/2311.17132v3 | Number of params | 49.7M |
Domain Adaptation > Domain Generalization | ImageNet-A | ConvFormer-B36 (384) | https://arxiv.org/abs/2210.13452v4 | Top-1 accuracy % | 55.3 |
Domain Adaptation > Domain Generalization | ImageNet-A | ConvFormer-B36 (384) | https://arxiv.org/abs/2210.13452v4 | Number of params | 100M |
Domain Adaptation > Domain Generalization | ImageNet-A | SEER (RegNet10B) | https://arxiv.org/abs/2202.08360v2 | Top-1 accuracy % | 52.7 |
Domain Adaptation > Domain Generalization | ImageNet-A | TransNeXt-Base (IN-1K supervised, 224) | https://arxiv.org/abs/2311.17132v3 | Top-1 accuracy % | 50.6 |
Domain Adaptation > Domain Generalization | ImageNet-A | TransNeXt-Base (IN-1K supervised, 224) | https://arxiv.org/abs/2311.17132v3 | Number of params | 89.7M |
Domain Adaptation > Domain Generalization | ImageNet-A | CAFormer-B36 | https://arxiv.org/abs/2210.13452v4 | Top-1 accuracy % | 48.5 |
Domain Adaptation > Domain Generalization | ImageNet-A | CAFormer-B36 | https://arxiv.org/abs/2210.13452v4 | Number of params | 99M |
Domain Adaptation > Domain Generalization | ImageNet-A | TransNeXt-Small (IN-1K supervised, 224) | https://arxiv.org/abs/2311.17132v3 | Top-1 accuracy % | 47.1 |
Domain Adaptation > Domain Generalization | ImageNet-A | TransNeXt-Small (IN-1K supervised, 224) | https://arxiv.org/abs/2311.17132v3 | Number of params | 49.7M |
Domain Adaptation > Domain Generalization | ImageNet-A | FAN-L-Hybrid+STL | https://arxiv.org/abs/2401.03844v1 | Top-1 accuracy % | 46.1 |
Domain Adaptation > Domain Generalization | ImageNet-A | ConvFormer-B36 | https://arxiv.org/abs/2210.13452v4 | Top-1 accuracy % | 40.1 |
Domain Adaptation > Domain Generalization | ImageNet-A | ConvFormer-B36 | https://arxiv.org/abs/2210.13452v4 | Number of params | 100M |
Domain Adaptation > Domain Generalization | ImageNet-A | Pyramid Adversarial Training Improves ViT (384x384) | https://arxiv.org/abs/2111.15121v2 | Top-1 accuracy % | 36.41 |
Domain Adaptation > Domain Generalization | ImageNet-A | Sequencer2D-L | https://arxiv.org/abs/2205.01972v4 | Top-1 accuracy % | 35.5 |
Domain Adaptation > Domain Generalization | ImageNet-A | Discrete Adversarial Distillation (ViT-B/224) | https://arxiv.org/abs/2311.01441v2 | Top-1 accuracy % | 31.8 |
Domain Adaptation > Domain Generalization | ImageNet-A | Diffusion Classifier | https://arxiv.org/abs/2303.16203v3 | Top-1 accuracy % | 30.2 |
Domain Adaptation > Domain Generalization | ImageNet-A | RVT-B* | https://arxiv.org/abs/2105.07926v4 | Top-1 accuracy % | 28.5 |
Domain Adaptation > Domain Generalization | ImageNet-A | RVT-S* | https://arxiv.org/abs/2105.07926v4 | Top-1 accuracy % | 25.7 |
Domain Adaptation > Domain Generalization | ImageNet-A | RVT-Ti* | https://arxiv.org/abs/2105.07926v4 | Top-1 accuracy % | 14.4 |
Domain Adaptation > Domain Generalization | ImageNet-A | GFNet-S | https://arxiv.org/abs/2107.00645v2 | Top-1 accuracy % | 14.3 |
Domain Adaptation > Domain Generalization | ImageNet-A | CutMix+MoEx (ResNet-50) | https://arxiv.org/abs/2002.11102v3 | Top-1 accuracy % | 8.4 |
Domain Adaptation > Domain Generalization | ImageNet-A | Discrete Adversarial Distillation (ResNet-50) | https://arxiv.org/abs/2311.01441v2 | Top-1 accuracy % | 7.7 |
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