Datasets:

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[ { "index": 2, "records": [ { "column": 3, "dataset": "COCO test-dev", "metric": "AP50", "model": "Cascade Mask R-CNN (Triple-ResNeXt152, multi-scale)", "row": 22, "task": "Object Detection", "value": "71.9" }, { "column": 5, "dataset": "COCO test-dev", "metric": "APS", "model": "Cascade Mask R-CNN (Triple-ResNeXt152, multi-scale)", "row": 22, "task": "Object Detection", "value": "35.5" } ] } ]

[ { "index": 2, "records": [ { "column": 1, "dataset": "VQA-CP", "metric": "Score", "model": "Learned-Mixin +H", "row": 5, "task": "Visual Question Answering", "value": "52.05" } ] } ]

[ { "index": 3, "records": [ { "column": 1, "dataset": "KITTI Tracking test", "metric": "MOTA", "model": "mmMOT-normal", "row": 7, "task": "Multiple Object Tracking", "value": "84.77" } ] } ]

[ { "index": 1, "records": [ { "column": 1, "dataset": "OMNIGLOT - 1-Shot, 5-way", "metric": "Accuracy", "model": "iMAML, Hessian-Free", "row": 5, "task": "Few-Shot Image Classification", "value": "99.50" }, { "column": 2, "dataset": "OMNIGLOT - 5-Shot, 5-way", "metric": "Accuracy", "model": "iMAML, Hessian-Free", "row": 5, "task": "Few-Shot Image Classification", "value": "99.74" }, { "column": 3, "dataset": "OMNIGLOT - 1-Shot, 20-way", "metric": "Accuracy", "model": "iMAML, Hessian-Free", "row": 5, "task": "Few-Shot Image Classification", "value": "96.18" }, { "column": 4, "dataset": "OMNIGLOT - 5-Shot, 20-way", "metric": "Accuracy", "model": "iMAML, Hessian-Free", "row": 5, "task": "Few-Shot Image Classification", "value": "99.14" } ] }, { "index": 2, "records": [ { "column": 1, "dataset": "Mini-Imagenet 5-way (1-shot)", "metric": "Accuracy", "model": "iMAML HF", "row": 5, "task": "Few-Shot Image Classification", "value": "49.30" } ] } ]

[ { "index": 1, "records": [ { "column": 1, "dataset": "MLDoc Zero-Shot English-to-German", "metric": "Accuracy", "model": "MultiFiT, pseudo", "row": 6, "task": "Cross-Lingual Document Classification", "value": "91.62" }, { "column": 2, "dataset": "MLDoc Zero-Shot English-to-Spanish", "metric": "Accuracy", "model": "MultiFiT, pseudo", "row": 6, "task": "Cross-Lingual Document Classification", "value": "79.1" }, { "column": 3, "dataset": "MLDoc Zero-Shot English-to-French", "metric": "Accuracy", "model": "MultiFiT, pseudo", "row": 6, "task": "Cross-Lingual Document Classification", "value": "89.42" }, { "column": 4, "dataset": "MLDoc Zero-Shot English-to-Italian", "metric": "Accuracy", "model": "MultiFiT, pseudo", "row": 6, "task": "Cross-Lingual Document Classification", "value": "76.02" }, { "column": 5, "dataset": "MLDoc Zero-Shot English-to-Japanese", "metric": "Accuracy", "model": "MultiFiT, pseudo", "row": 6, "task": "Cross-Lingual Document Classification", "value": "69.57" }, { "column": 6, "dataset": "MLDoc Zero-Shot English-to-Russian", "metric": "Accuracy", "model": "MultiFiT, pseudo", "row": 6, "task": "Cross-Lingual Document Classification", "value": "67.83" }, { "column": 7, "dataset": "MLDoc Zero-Shot English-to-Chinese", "metric": "Accuracy", "model": "MultiFiT, pseudo", "row": 6, "task": "Cross-Lingual Document Classification", "value": "82.48" } ] } ]

[ { "index": 6, "records": [ { "column": 4, "dataset": "COCO minival", "metric": "APS", "model": "FCOS (ResNet-50-FPN + improvements)", "row": 6, "task": "Object Detection", "value": "22.3" } ] } ]

[ { "index": 0, "records": [ { "column": 7, "dataset": "Set5 - 4x upscaling", "metric": "PSNR", "model": "Edge-informed SR", "row": 5, "task": "Image Super-Resolution", "value": "28.59" }, { "column": 7, "dataset": "Set14 - 4x upscaling", "metric": "PSNR", "model": "Edge-informed SR", "row": 6, "task": "Image Super-Resolution", "value": "25.19" }, { "column": 7, "dataset": "BSD100 - 4x upscaling", "metric": "PSNR", "model": "Edge-informed SR", "row": 7, "task": "Image Super-Resolution", "value": "24.25" }, { "column": 7, "dataset": "Celeb-HQ 4x upscaling", "metric": "PSNR", "model": "Edge-informed SR", "row": 8, "task": "Image Super-Resolution", "value": "28.23" }, { "column": 7, "dataset": "Set5 - 4x upscaling", "metric": "SSIM", "model": "Edge-informed SR", "row": 17, "task": "Image Super-Resolution", "value": "0.965" }, { "column": 7, "dataset": "Set14 - 4x upscaling", "metric": "SSIM", "model": "Edge-informed SR", "row": 18, "task": "Image Super-Resolution", "value": "0.894" }, { "column": 7, "dataset": "BSD100 - 4x upscaling", "metric": "SSIM", "model": "Edge-informed SR", "row": 19, "task": "Image Super-Resolution", "value": "0.851" }, { "column": 7, "dataset": "Celeb-HQ 4x upscaling", "metric": "SSIM", "model": "Edge-informed SR", "row": 20, "task": "Image Super-Resolution", "value": "0.912" } ] } ]

[ { "index": 1, "records": [ { "column": 4, "dataset": "Flickr30K 1K test", "metric": "R@1", "model": "CAMP", "row": 10, "task": "Image Retrieval", "value": "51.5" }, { "column": 5, "dataset": "Flickr30K 1K test", "metric": "R@5", "model": "CAMP", "row": 10, "task": "Image Retrieval", "value": "77.1" }, { "column": 6, "dataset": "Flickr30K 1K test", "metric": "R@10", "model": "CAMP", "row": 10, "task": "Image Retrieval", "value": "85.3" } ] } ]

[ { "index": 3, "records": [ { "column": 4, "dataset": "BSD68 sigma15", "metric": "PSNR", "model": "CSCNet", "row": 1, "task": "Color Image Denoising", "value": "33.83" }, { "column": 4, "dataset": "BSD68 sigma25", "metric": "PSNR", "model": "CSCNet", "row": 2, "task": "Color Image Denoising", "value": "31.18" }, { "column": 4, "dataset": "BSD68 sigma50", "metric": "PSNR", "model": "CSCNet", "row": 3, "task": "Color Image Denoising", "value": "28" }, { "column": 4, "dataset": "BSD68 sigma75", "metric": "PSNR", "model": "CSCNet", "row": 4, "task": "Color Image Denoising", "value": "26.32" } ] } ]

[ { "index": 4, "records": [ { "column": 2, "dataset": "Cityscapes test", "metric": "Mean IoU (class)", "model": "DGCNet (ResNet-101)", "row": 16, "task": "Semantic Segmentation", "value": "82" } ] }, { "index": 5, "records": [ { "column": 2, "dataset": "PASCAL Context", "metric": "mIoU", "model": "DGCNet (MS, ResNet-101)", "row": 16, "task": "Semantic Segmentation", "value": "53.7" } ] } ]

[ { "index": 2, "records": [ { "column": 4, "dataset": "LibriSpeech test-other", "metric": "Word Error Rate (WER)", "model": "Transformer", "row": 2, "task": "Speech Recognition", "value": "5.7" }, { "column": 3, "dataset": "LibriSpeech test-clean", "metric": "Word Error Rate (WER)", "model": "Transformer", "row": 4, "task": "Speech Recognition", "value": "2.6" } ] } ]

[ { "index": 1, "records": [ { "column": 3, "dataset": "VOT2017", "metric": "Expected Average Overlap (EAO)", "model": "GradNet", "row": 1, "task": "Visual Object Tracking", "value": "0.247" } ] }, { "index": 2, "records": [ { "column": 1, "dataset": "OTB-2015", "metric": "Precision", "model": "GradNet", "row": 1, "task": "Visual Object Tracking", "value": "0.861" } ] } ]

[ { "index": 0, "records": [ { "column": 2, "dataset": "MLDoc Zero-Shot English-to-French", "metric": "Error rate", "model": "XLMft UDA", "row": 10, "task": "Cross-Lingual Sentiment Classification", "value": "5.95" }, { "column": 3, "dataset": "MLDoc Zero-Shot English-to-German", "metric": "Error rate", "model": "XLMft UDA", "row": 10, "task": "Cross-Lingual Sentiment Classification", "value": "6.12" }, { "column": 4, "dataset": "MLDoc Zero-Shot English-to-Chinese", "metric": "Error rate", "model": "XLMft UDA", "row": 10, "task": "Cross-Lingual Sentiment Classification", "value": "7.74" }, { "column": 5, "dataset": "Dianping (Yelp train)", "metric": "Error rate", "model": "XLMft UDA", "row": 10, "task": "Cross-Lingual Sentiment Classification", "value": "4.64" } ] }, { "index": 1, "records": [ { "column": 2, "dataset": "MLDoc Zero-Shot English-to-French", "metric": "Error rate", "model": "XLMft UDA", "row": 11, "task": "Cross-Lingual Document Classification", "value": "3.95" }, { "column": 3, "dataset": "MLDoc Zero-Shot English-to-German", "metric": "Error rate", "model": "XLMft UDA", "row": 11, "task": "Cross-Lingual Document Classification", "value": "3.05" }, { "column": 4, "dataset": "MLDoc Zero-Shot English-to-Spanish", "metric": "Error rate", "model": "XLMft UDA", "row": 11, "task": "Cross-Lingual Document Classification", "value": "3.2" }, { "column": 5, "dataset": "MLDoc Zero-Shot English-to-Chinese", "metric": "Error rate", "model": "XLMft UDA", "row": 11, "task": "Cross-Lingual Document Classification", "value": "6.68" }, { "column": 6, "dataset": "MLDoc Zero-Shot English-to-Russian", "metric": "Error rate", "model": "XLMft UDA", "row": 11, "task": "Cross-Lingual Document Classification", "value": "10.3" } ] }, { "index": 7, "records": [ { "column": 2, "dataset": "Yelp (Amazon en train)", "metric": "Error rate", "model": "XLMft UDA", "row": 10, "task": "Cross-Domain Document Classification", "value": "3.34" }, { "column": 3, "dataset": "Amazon en (Yelp train)", "metric": "Error rate", "model": "XLMft UDA", "row": 10, "task": "Cross-Domain Document Classification", "value": "7.57" }, { "column": 4, "dataset": "Dianping (Amazon cn train)", "metric": "Error rate", "model": "XLMft UDA", "row": 10, "task": "Cross-Domain Document Classification", "value": "4.64" }, { "column": 5, "dataset": "Amazon cn (Dianping train)", "metric": "Error rate", "model": "XLMft UDA", "row": 10, "task": "Cross-Domain Document Classification", "value": "7.74" } ] } ]

[ { "index": 0, "records": [ { "column": 1, "dataset": "CUB", "metric": "Inception score", "model": "Attention-driven Generator (perceptual loss)", "row": 4, "task": "Text-to-Image Generation", "value": "4.58" }, { "column": 4, "dataset": "COCO", "metric": "Inception score", "model": "Attention-driven Generator (perceptual loss)", "row": 4, "task": "Text-to-Image Generation", "value": "24.06" } ] } ]

[ { "index": 6, "records": [ { "column": 2, "dataset": "Cityscapes test", "metric": "Mean IoU (class)", "model": "GALDNet(+Mapillary)++", "row": 6, "task": "Semantic Segmentation", "value": "83.3" } ] }, { "index": 11, "records": [ { "column": 21, "dataset": "PASCAL VOC 2007", "metric": "Mean IoU", "model": "GALDNet", "row": 5, "task": "Semantic Segmentation", "value": "83" } ] } ]

[ { "index": 0, "records": [ { "column": 1, "dataset": "MNIST-to-USPS", "metric": "Accuracy", "model": "3CATN", "row": 9, "task": "Domain Adaptation", "value": "96.1" }, { "column": 2, "dataset": "USPS-to-MNIST", "metric": "Accuracy", "model": "3CATN", "row": 9, "task": "Domain Adaptation", "value": "98.3" }, { "column": 3, "dataset": "SVNH-to-MNIST", "metric": "Accuracy", "model": "3CATN", "row": 9, "task": "Domain Adaptation", "value": "92.5" } ] } ]

[ { "index": 2, "records": [ { "column": 2, "dataset": "WSJ eval92", "metric": "Word Error Rate (WER)", "model": "Espresso", "row": 9, "task": "Speech Recognition", "value": "3.4" } ] }, { "index": 4, "records": [ { "column": 3, "dataset": "LibriSpeech test-clean", "metric": "Word Error Rate (WER)", "model": "Espresso", "row": 9, "task": "Speech Recognition", "value": "2.8" }, { "column": 4, "dataset": "LibriSpeech test-other", "metric": "Word Error Rate (WER)", "model": "Espresso", "row": 9, "task": "Speech Recognition", "value": "8.7" } ] }, { "index": 5, "records": [ { "column": 1, "dataset": "Hub5'00 SwitchBoard", "metric": "Word Error Rate (WER)", "model": "Espresso", "row": 5, "task": "Speech Recognition", "value": "9.2" }, { "column": 2, "dataset": "Hub5'00 CallHome", "metric": "Word Error Rate (WER)", "model": "Espresso", "row": 5, "task": "Speech Recognition", "value": "19.1" } ] } ]

[ { "index": 0, "records": [ { "column": 1, "dataset": "Point Maze", "metric": "Average Return", "model": "PEMIRL", "row": 6, "task": "MuJoCo Games", "value": "-7.37" }, { "column": 2, "dataset": "Ant", "metric": "Average Return", "model": "PEMIRL", "row": 6, "task": "MuJoCo Games", "value": "846.18" }, { "column": 3, "dataset": "Sweeper", "metric": "Average Return", "model": "PEMIRL", "row": 6, "task": "MuJoCo Games", "value": "-74.17" }, { "column": 4, "dataset": "Sawyer Pusher", "metric": "Average Return", "model": "PEMIRL", "row": 6, "task": "MuJoCo Games", "value": "-27.16" } ] } ]

[ { "index": 8, "records": [ { "column": 1, "dataset": "AG News", "metric": "Accuracy", "model": "ULMFiT (Small data)", "row": 8, "task": "Text Classification", "value": "93.7" }, { "column": 2, "dataset": "DBpedia", "metric": "Accuracy", "model": "ULMFiT (Small data)", "row": 8, "task": "Text Classification", "value": "99.2" }, { "column": 3, "dataset": "Sogou News", "metric": "Accuracy", "model": "ULMFiT (Small data)", "row": 8, "task": "Text Classification", "value": "97" }, { "column": 4, "dataset": "Yelp-5", "metric": "Accuracy", "model": "ULMFiT (Small data)", "row": 8, "task": "Text Classification", "value": "67.6" }, { "column": 5, "dataset": "Yelp-2", "metric": "Accuracy", "model": "ULMFiT (Small data)", "row": 8, "task": "Text Classification", "value": "97.1" }, { "column": 6, "dataset": "Yahoo! Answers", "metric": "Accuracy", "model": "ULMFiT (Small data)", "row": 8, "task": "Text Classification", "value": "74.3" }, { "column": 7, "dataset": "Amazon-2", "metric": "Error", "model": "ULMFiT (Small data)", "row": 8, "task": "Text Classification", "value": "3.9" }, { "column": 8, "dataset": "Amazon-5", "metric": "Error", "model": "ULMFiT (Small data)", "row": 8, "task": "Text Classification", "value": "35.9" } ] } ]

[ { "index": 0, "records": [ { "column": 1, "dataset": "USR-248 - 4x upscaling", "metric": "PSNR", "model": "SRDRM-GAN", "row": 6, "task": "Image Super-Resolution", "value": "24.62" }, { "column": 2, "dataset": "USR-248 - 4x upscaling", "metric": "SSIM", "model": "SRDRM-GAN", "row": 6, "task": "Image Super-Resolution", "value": "0.69" }, { "column": 3, "dataset": "USR-248 - 4x upscaling", "metric": "UIQM", "model": "SRDRM-GAN", "row": 6, "task": "Image Super-Resolution", "value": "2.48" } ] } ]

[ { "index": 0, "records": [ { "column": 1, "dataset": "HVSMR 2016", "metric": "Dice Score", "model": "StyleSegor (adjusted)", "row": 12, "task": "Cardiovascular MR Segmentaiton", "value": "0.825" }, { "column": 2, "dataset": "HVSMR 2016", "metric": "Dice Score", "model": "StyleSegor (adjusted)", "row": 12, "task": "Cardiovascular MR Segmentaiton", "value": "0.934" }, { "column": 3, "dataset": "HVSMR 2016", "metric": "Hausdorff Distance (mm)", "model": "StyleSegor (adjusted)", "row": 12, "task": "Cardiovascular MR Segmentaiton", "value": "4.633" }, { "column": 4, "dataset": "HVSMR 2016", "metric": "Dice Score", "model": "StyleSegor (adjusted)", "row": 12, "task": "Cardiovascular MR Segmentaiton", "value": "0.923" }, { "column": 5, "dataset": "HVSMR 2016", "metric": "Hausdorff Distance (mm)", "model": "StyleSegor (adjusted)", "row": 12, "task": "Cardiovascular MR Segmentaiton", "value": "1.073" }, { "column": 6, "dataset": "HVSMR 2016", "metric": "Hausdorff Distance (mm)", "model": "StyleSegor (adjusted)", "row": 12, "task": "Cardiovascular MR Segmentaiton", "value": "7.435" }, { "column": 7, "dataset": "HVSMR 2016", "metric": "Dice Score", "model": "StyleSegor (adjusted)", "row": 12, "task": "Cardiovascular MR Segmentaiton", "value": "-0.03" } ] } ]

[ { "index": 1, "records": [ { "column": 1, "dataset": "MultiNLI", "metric": "Matched", "model": "TinyBERT", "row": 7, "task": "Natural Language Inference", "value": "82.5" }, { "column": 2, "dataset": "MultiNLI", "metric": "Mismatched", "model": "TinyBERT", "row": 7, "task": "Natural Language Inference", "value": "81.8" }, { "column": 3, "dataset": "Quora Question Pairs", "metric": "Accuracy", "model": "TinyBERT", "row": 7, "task": "Paraphrase Identification", "value": "71.3" }, { "column": 4, "dataset": "SST-2 Binary classification", "metric": "Accuracy", "model": "TinyBERT", "row": 7, "task": "Sentiment Analysis", "value": "92.6" }, { "column": 5, "dataset": "QNLI", "metric": "Accuracy", "model": "TinyBERT", "row": 7, "task": "Natural Language Inference", "value": "87.7" }, { "column": 6, "dataset": "MRPC", "metric": "Accuracy", "model": "TinyBERT", "row": 7, "task": "Semantic Textual Similarity", "value": "86.4" }, { "column": 7, "dataset": "RTE", "metric": "Accuracy", "model": "TinyBERT", "row": 7, "task": "Natural Language Inference", "value": "62.9" }, { "column": 8, "dataset": "CoLA", "metric": "Accuracy", "model": "TinyBERT", "row": 7, "task": "Linguistic Acceptability", "value": "43.3" }, { "column": 9, "dataset": "STS Benchmark", "metric": "Pearson Correlation", "model": "TinyBERT", "row": 7, "task": "Semantic Textual Similarity", "value": "0.799" } ] }, { "index": 3, "records": [ { "column": 1, "dataset": "MultiNLI Dev", "metric": "Matched", "model": "TinyBERT (M=6;d'=768;d'i=3072)", "row": 7, "task": "Natural Language Inference", "value": "84.5" }, { "column": 2, "dataset": "MultiNLI Dev", "metric": "Mismatched", "model": "TinyBERT (M=6;d'=768;d'i=3072)", "row": 7, "task": "Natural Language Inference", "value": "84.5" }, { "column": 3, "dataset": "MRPC Dev", "metric": "Accuracy", "model": "TinyBERT (M=6;d'=768;d'i=3072)", "row": 7, "task": "Semantic Textual Similarity", "value": "86.3" }, { "column": 4, "dataset": "CoLA Dev", "metric": "Accuracy", "model": "TinyBERT (M=6;d' =768;d'i=3072)", "row": 7, "task": "Linguistic Acceptability", "value": "54" } ] }, { "index": 7, "records": [ { "column": 1, "dataset": "SQuAD1.1 dev", "metric": "EM", "model": "TinyBERT (M=6;d' =768;d'i=3072)", "row": 10, "task": "Question Answering", "value": "79.7" }, { "column": 2, "dataset": "SQuAD1.1 dev", "metric": "F1", "model": "TinyBERT (M=6;d' =768;d'i=3072)", "row": 10, "task": "Question Answering", "value": "87.5" }, { "column": 3, "dataset": "SQuAD2.0 dev", "metric": "EM", "model": "TinyBERT (M=6;d' =768;d'i=3072)", "row": 10, "task": "Question Answering", "value": "69.9" }, { "column": 4, "dataset": "SQuAD2.0 dev", "metric": "F1", "model": "TinyBERT (M=6;d' =768;d'i=3072)", "row": 10, "task": "Question Answering", "value": "73.4" } ] } ]

[ { "index": 1, "records": [ { "column": 1, "dataset": "COCO Captions test", "metric": "BLEU-4", "model": "Unified VLP", "row": 17, "task": "Image Captioning", "value": "36.5" }, { "column": 2, "dataset": "COCO Captions test", "metric": "METEOR", "model": "Unified VLP", "row": 17, "task": "Image Captioning", "value": "28.4" }, { "column": 3, "dataset": "COCO Captions test", "metric": "CIDEr", "model": "Unified VLP", "row": 17, "task": "Image Captioning", "value": "116.9" }, { "column": 4, "dataset": "COCO Captions test", "metric": "SPICE", "model": "Unified VLP", "row": 17, "task": "Image Captioning", "value": "21.2" }, { "column": 5, "dataset": "VQA v2 test-std", "metric": "Accuracy", "model": "Unified VLP", "row": 17, "task": "Visual Question Answering", "value": "70.7" }, { "column": 9, "dataset": "Flickr30k Captions test", "metric": "BLEU-4", "model": "Unified VLP", "row": 17, "task": "Image Captioning", "value": "30.1" }, { "column": 10, "dataset": "Flickr30k Captions test", "metric": "METEOR", "model": "Unified VLP", "row": 17, "task": "Image Captioning", "value": "23" }, { "column": 11, "dataset": "Flickr30k Captions test", "metric": "CIDEr", "model": "Unified VLP", "row": 17, "task": "Image Captioning", "value": "67.4" }, { "column": 12, "dataset": "Flickr30k Captions test", "metric": "SPICE", "model": "Unified VLP", "row": 17, "task": "Image Captioning", "value": "17" } ] } ]

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