Datasets:

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[ { "index": 1, "records": [ { "column": 2, "dataset": "Set5 - 2x upscaling", "metric": "PSNR", "model": "HBPN", "row": 12, "task": "Image Super-Resolution", "value": "38.13" }, { "column": 3, "dataset": "Set5 - 2x upscaling", "metric": "SSIM", "model": "HBPN", "row": 12, "task": "Image Super-Resolution", "value": "0.961" }, { "column": 4, "dataset": "Set14 - 2x upscaling", "metric": "PSNR", "model": "HBPN", "row": 12, "task": "Image Super-Resolution", "value": "33.78" }, { "column": 5, "dataset": "Set14 - 2x upscaling", "metric": "SSIM", "model": "HBPN", "row": 12, "task": "Image Super-Resolution", "value": "0.9209999999999999" }, { "column": 6, "dataset": "BSD100 - 2x upscaling", "metric": "PSNR", "model": "HBPN", "row": 12, "task": "Image Super-Resolution", "value": "32.33" }, { "column": 7, "dataset": "BSD100 - 2x upscaling", "metric": "SSIM", "model": "HBPN", "row": 12, "task": "Image Super-Resolution", "value": "0.902" }, { "column": 8, "dataset": "Urban100 - 2x upscaling", "metric": "PSNR", "model": "HBPN", "row": 12, "task": "Image Super-Resolution", "value": "33.12" }, { "column": 9, "dataset": "Urban100 - 2x upscaling", "metric": "SSIM", "model": "HBPN", "row": 12, "task": "Image Super-Resolution", "value": "0.938" }, { "column": 10, "dataset": "Manga109 - 2x upscaling", "metric": "PSNR", "model": "HBPN", "row": 12, "task": "Image Super-Resolution", "value": "39.3" }, { "column": 11, "dataset": "Manga109 - 2x upscaling", "metric": "SSIM", "model": "HBPN", "row": 12, "task": "Image Super-Resolution", "value": "0.9790000000000001" }, { "column": 2, "dataset": "Set5 - 4x upscaling", "metric": "PSNR", "model": "HBPN", "row": 23, "task": "Image Super-Resolution", "value": "32.55" }, { "column": 3, "dataset": "Set5 - 4x upscaling", "metric": "SSIM", "model": "HBPN", "row": 23, "task": "Image Super-Resolution", "value": "0.9" }, { "column": 4, "dataset": "Set14 - 4x upscaling", "metric": "PSNR", "model": "HBPN", "row": 23, "task": "Image Super-Resolution", "value": "28.67" }, { "column": 5, "dataset": "Set14 - 4x upscaling", "metric": "SSIM", "model": "HBPN", "row": 23, "task": "Image Super-Resolution", "value": "0.785" }, { "column": 6, "dataset": "BSD100 - 4x upscaling", "metric": "PSNR", "model": "HBPN", "row": 23, "task": "Image Super-Resolution", "value": "27.77" }, { "column": 7, "dataset": "BSD100 - 4x upscaling", "metric": "SSIM", "model": "HBPN", "row": 23, "task": "Image Super-Resolution", "value": "0.743" }, { "column": 8, "dataset": "Urban100 - 4x upscaling", "metric": "PSNR", "model": "HBPN", "row": 23, "task": "Image Super-Resolution", "value": "27.3" }, { "column": 9, "dataset": "Urban100 - 4x upscaling", "metric": "SSIM", "model": "HBPN", "row": 23, "task": "Image Super-Resolution", "value": "0.818" }, { "column": 10, "dataset": "Manga109 - 4x upscaling", "metric": "PSNR", "model": "HBPN", "row": 23, "task": "Image Super-Resolution", "value": "31.57" }, { "column": 11, "dataset": "Manga109 - 4x upscaling", "metric": "SSIM", "model": "HBPN", "row": 23, "task": "Image Super-Resolution", "value": "0.92" }, { "column": 2, "dataset": "Set5 - 8x upscaling", "metric": "PSNR", "model": "HBPN", "row": 34, "task": "Image Super-Resolution", "value": "27.17" }, { "column": 3, "dataset": "Set5 - 8x upscaling", "metric": "SSIM", "model": "HBPN", "row": 34, "task": "Image Super-Resolution", "value": "0.785" }, { "column": 4, "dataset": "Set14 - 8x upscaling", "metric": "PSNR", "model": "HBPN", "row": 34, "task": "Image Super-Resolution", "value": "24.96" }, { "column": 5, "dataset": "Set14 - 8x upscaling", "metric": "SSIM", "model": "HBPN", "row": 34, "task": "Image Super-Resolution", "value": "0.642" }, { "column": 6, "dataset": "BSD100 - 8x upscaling", "metric": "PSNR", "model": "HBPN", "row": 34, "task": "Image Super-Resolution", "value": "24.93" }, { "column": 7, "dataset": "BSD100 - 8x upscaling", "metric": "SSIM", "model": "HBPN", "row": 34, "task": "Image Super-Resolution", "value": "0.602" }, { "column": 8, "dataset": "Urban100 - 8x upscaling", "metric": "PSNR", "model": "HBPN", "row": 34, "task": "Image Super-Resolution", "value": "23.04" }, { "column": 9, "dataset": "Urban100 - 8x upscaling", "metric": "SSIM", "model": "HBPN", "row": 34, "task": "Image Super-Resolution", "value": "0.647" }, { "column": 10, "dataset": "Manga109 - 8x upscaling", "metric": "PSNR", "model": "HBPN", "row": 34, "task": "Image Super-Resolution", "value": "25.24" }, { "column": 11, "dataset": "Manga109 - 8x upscaling", "metric": "SSIM", "model": "HBPN", "row": 34, "task": "Image Super-Resolution", "value": "0.802" } ] } ]

[ { "index": 0, "records": [ { "column": 1, "dataset": "VggFace2 - 8x upscaling", "metric": "PSNR", "model": "Full-GWAInet", "row": 8, "task": "Image Super-Resolution", "value": "25.57" }, { "column": 2, "dataset": "WebFace - 8x upscaling", "metric": "PSNR", "model": "Full-GWAInet", "row": 8, "task": "Image Super-Resolution", "value": "27.11" } ] } ]

[ { "index": 3, "records": [ { "column": 1, "dataset": "CMRC 2018 (Simplified Chinese) Dev", "metric": "EM", "model": "RoBERTa-wwm-ext-large", "row": 7, "task": "Chinese Reading Comprehension", "value": "68.5" }, { "column": 2, "dataset": "CMRC 2018 (Simplified Chinese) Dev", "metric": "F1", "model": "RoBERTa-wwm-ext-large", "row": 7, "task": "Chinese Reading Comprehension", "value": "88.4" }, { "column": 3, "dataset": "CMRC 2018 (Simplified Chinese)", "metric": "EM", "model": "RoBERTa-wwm-ext-large", "row": 7, "task": "Chinese Reading Comprehension", "value": "74.2" }, { "column": 4, "dataset": "CMRC 2018 (Simplified Chinese)", "metric": "F1", "model": "RoBERTa-wwm-ext-large", "row": 7, "task": "Chinese Reading Comprehension", "value": "90.6" }, { "column": 5, "dataset": "CMRC 2018 (Simplified Chinese) Challenge", "metric": "EM", "model": "RoBERTa-wwm-ext-large", "row": 7, "task": "Chinese Reading Comprehension", "value": "31.5" }, { "column": 6, "dataset": "CMRC 2018 (Simplified Chinese) Challenge", "metric": "F1", "model": "RoBERTa-wwm-ext-large", "row": 7, "task": "Chinese Reading Comprehension", "value": "60.1" } ] }, { "index": 4, "records": [ { "column": 1, "dataset": "DRCD (Traditional Chinese) Dev", "metric": "EM", "model": "RoBERTa-wwm-ext-large", "row": 7, "task": "Chinese Reading Comprehension", "value": "89.6" }, { "column": 2, "dataset": "DRCD (Traditional Chinese) Dev", "metric": "F1", "model": "RoBERTa-wwm-ext-large", "row": 7, "task": "Chinese Reading Comprehension", "value": "94.8" }, { "column": 3, "dataset": "DRCD (Traditional Chinese)", "metric": "EM", "model": "RoBERTa-wwm-ext-large", "row": 7, "task": "Chinese Reading Comprehension", "value": "89.6" }, { "column": 4, "dataset": "DRCD (Traditional Chinese)", "metric": "F1", "model": "RoBERTa-wwm-ext-large", "row": 7, "task": "Chinese Reading Comprehension", "value": "94.5" } ] }, { "index": 5, "records": [ { "column": 1, "dataset": "CJRC Dev", "metric": "EM", "model": "RoBERTa-wwm-ext-large", "row": 7, "task": "Chinese Reading Comprehension", "value": "62.1" }, { "column": 2, "dataset": "CJRC Dev", "metric": "F1", "model": "RoBERTa-wwm-ext-large", "row": 7, "task": "Chinese Reading Comprehension", "value": "82.4" }, { "column": 3, "dataset": "CJRC", "metric": "EM", "model": "RoBERTa-wwm-ext-large", "row": 7, "task": "Chinese Reading Comprehension", "value": "62.4" }, { "column": 4, "dataset": "CJRC", "metric": "F1", "model": "RoBERTa-wwm-ext-large", "row": 7, "task": "Chinese Reading Comprehension", "value": "82.20" } ] }, { "index": 6, "records": [ { "column": 1, "dataset": "XNLI Dev", "metric": "F1", "model": "RoBERTa-wwm-ext-large", "row": 6, "task": "Chinese Sentence Pair Classification", "value": "82.1" }, { "column": 2, "dataset": "XNLI", "metric": "F1", "model": "RoBERTa-wwm-ext-large", "row": 6, "task": "Chinese Sentence Pair Classification", "value": "81.2" } ] }, { "index": 7, "records": [ { "column": 1, "dataset": "ChnSentiCorp Dev", "metric": "F1", "model": "RoBERTa-wwm-ext-large", "row": 6, "task": "Sentiment Analysis", "value": "95.8" }, { "column": 2, "dataset": "ChnSentiCorp", "metric": "F1", "model": "RoBERTa-wwm-ext-large", "row": 6, "task": "Sentiment Analysis", "value": "95.8" } ] }, { "index": 8, "records": [ { "column": 1, "dataset": "LCQMC Dev", "metric": "F1", "model": "RoBERTa-wwm-ext-large", "row": 7, "task": "Chinese Sentence Pair Classification", "value": "90.4" }, { "column": 2, "dataset": "LCQMC", "metric": "F1", "model": "RoBERTa-wwm-ext-large", "row": 7, "task": "Chinese Sentence Pair Classification", "value": "87" }, { "column": 3, "dataset": "BQ Dev", "metric": "F1", "model": "RoBERTa-wwm-ext-large", "row": 7, "task": "Chinese Sentence Pair Classification", "value": "86.3" }, { "column": 4, "dataset": "BQ", "metric": "F1", "model": "RoBERTa-wwm-ext-large", "row": 7, "task": "Chinese Sentence Pair Classification", "value": "85.8" } ] }, { "index": 9, "records": [ { "column": 1, "dataset": "THUCNews Dev", "metric": "F1", "model": "RoBERTa-wwm-ext-large", "row": 6, "task": "Chinese Document Classification", "value": "98.3" }, { "column": 2, "dataset": "THUCNews", "metric": "F1", "model": "RoBERTa-wwm-ext-large", "row": 6, "task": "Chinese Document Classification", "value": "97.8" } ] } ]

[ { "index": 1, "records": [ { "column": 1, "dataset": "RACE", "metric": "Accuracy", "model": "XLNet", "row": 5, "task": "Reading Comprehension", "value": "85.4" }, { "column": 2, "dataset": "RACE", "metric": "Accuracy (Middle)", "model": "XLNet", "row": 5, "task": "Reading Comprehension", "value": "88.6" }, { "column": 3, "dataset": "RACE", "metric": "Accuracy (High)", "model": "XLNet", "row": 5, "task": "Reading Comprehension", "value": "84.0" }, { "column": 5, "dataset": "ClueWeb09-B", "metric": "nDCG@20", "model": "XLNet", "row": 5, "task": "Document Ranking", "value": "31.10" }, { "column": 6, "dataset": "ClueWeb09-B", "metric": "ERR@20", "model": "XLNet", "row": 5, "task": "Document Ranking", "value": "20.28" } ] }, { "index": 2, "records": [ { "column": 1, "dataset": "SQuAD2.0 dev", "metric": "EM", "model": "XLNet (single model)", "row": 4, "task": "Question Answering", "value": "87.9" }, { "column": 2, "dataset": "SQuAD2.0 dev", "metric": "F1", "model": "XLNet (single model)", "row": 4, "task": "Question Answering", "value": "90.6" }, { "column": 4, "dataset": "SQuAD1.1 dev", "metric": "EM", "model": "XLNet (single model)", "row": 4, "task": "Question Answering", "value": "89.7" }, { "column": 5, "dataset": "SQuAD1.1 dev", "metric": "F1", "model": "XLNet (single model)", "row": 4, "task": "Question Answering", "value": "95.1" }, { "column": 1, "dataset": "SQuAD2.0", "metric": "EM", "model": "XLNet (single model)", "row": 8, "task": "Question Answering", "value": "87.926" }, { "column": 2, "dataset": "SQuAD2.0", "metric": "F1", "model": "XLNet (single model)", "row": 8, "task": "Question Answering", "value": "90.689" }, { "column": 4, "dataset": "SQuAD1.1", "metric": "EM", "model": "XLNet (single model)", "row": 8, "task": "Question Answering", "value": "89.898" }, { "column": 5, "dataset": "SQuAD1.1", "metric": "F1", "model": "XLNet (single model)", "row": 8, "task": "Question Answering", "value": "95.080" } ] }, { "index": 3, "records": [ { "column": 1, "dataset": "IMDb", "metric": "Accuracy", "model": "XLNet", "row": 6, "task": "Text Classification", "value": "96.8" }, { "column": 2, "dataset": "Yelp-2", "metric": "Accuracy", "model": "XLNet", "row": 6, "task": "Text Classification", "value": "98.63" }, { "column": 3, "dataset": "Yelp-5", "metric": "Accuracy", "model": "XLNet", "row": 6, "task": "Text Classification", "value": "72.95" }, { "column": 4, "dataset": "DBpedia", "metric": "Error", "model": "XLNet", "row": 6, "task": "Text Classification", "value": "0.6" }, { "column": 5, "dataset": "AG News", "metric": "Error", "model": "XLNet", "row": 6, "task": "Text Classification", "value": "4.45" }, { "column": 6, "dataset": "Amazon-2", "metric": "Error", "model": "XLNet", "row": 6, "task": "Text Classification", "value": "2.11" }, { "column": 7, "dataset": "Amazon-5", "metric": "Error", "model": "XLNet", "row": 6, "task": "Text Classification", "value": "31.67" } ] }, { "index": 4, "records": [ { "column": 1, "dataset": "MultiNLI", "metric": "Matched", "model": "XLNet (single model)", "row": 4, "task": "Natural Language Inference", "value": "90.8" }, { "column": 2, "dataset": "QNLI", "metric": "Accuracy", "model": "XLNet (single model)", "row": 4, "task": "Natural Language Inference", "value": "94.9" }, { "column": 3, "dataset": "Quora Question Pairs", "metric": "Accuracy", "model": "XLNet (single model)", "row": 4, "task": "Question Answering", "value": "92.3" }, { "column": 4, "dataset": "RTE", "metric": "Accuracy", "model": "XLNet (single model)", "row": 4, "task": "Natural Language Inference", "value": "85.9" }, { "column": 5, "dataset": "SST-2 Binary classification", "metric": "Accuracy", "model": "XLNet (single model)", "row": 4, "task": "Sentiment Analysis", "value": "97" }, { "column": 6, "dataset": "MRPC", "metric": "Accuracy", "model": "XLNet (single model)", "row": 4, "task": "Semantic Textual Similarity", "value": "90.8" }, { "column": 7, "dataset": "CoLA", "metric": "Accuracy", "model": "XLNet (single model)", "row": 4, "task": "Linguistic Acceptability", "value": "69" }, { "column": 8, "dataset": "STS Benchmark", "metric": "Pearson Correlation", "model": "XLNet (single model)", "row": 4, "task": "Semantic Textual Similarity", "value": "92.5" } ] } ]

[ { "index": 8, "records": [ { "column": 2, "dataset": "Market-1501", "metric": "MAP", "model": "IBN-Net50-a", "row": 10, "task": "Person Re-Identification", "value": "88.2" }, { "column": 4, "dataset": "DukeMTMC-reID", "metric": "MAP", "model": "IBN-Net50-a", "row": 10, "task": "Person Re-Identification", "value": "79.1" } ] } ]

[ { "index": 2, "records": [ { "column": 7, "dataset": "MovieLens 20M", "metric": "Recall@10", "model": "HGN", "row": 2, "task": "Recommendation Systems", "value": "0.1255" }, { "column": 7, "dataset": "Amazon-Book", "metric": "Recall@10", "model": "HGN", "row": 3, "task": "Recommendation Systems", "value": "0.0429" }, { "column": 7, "dataset": "Amazon-CDs", "metric": "Recall@10", "model": "HGN", "row": 4, "task": "Recommendation Systems", "value": "0.0426" }, { "column": 7, "dataset": "GoodReads-Children", "metric": "Recall@10", "model": "HGN", "row": 5, "task": "Recommendation Systems", "value": "0.1263" }, { "column": 7, "dataset": "GoodReads-Comics", "metric": "Recall@10", "model": "HGN", "row": 6, "task": "Recommendation Systems", "value": "0.1743" }, { "column": 7, "dataset": "MovieLens 20M", "metric": "nDCG@10", "model": "HGN", "row": 8, "task": "Recommendation Systems", "value": "0.1195" }, { "column": 7, "dataset": "Amazon-Book", "metric": "nDCG@10", "model": "HGN", "row": 9, "task": "Recommendation Systems", "value": "0.0298" }, { "column": 7, "dataset": "Amazon-CDs", "metric": "nDCG@10", "model": "HGN", "row": 10, "task": "Recommendation Systems", "value": "0.0233" }, { "column": 7, "dataset": "GoodReads-Children", "metric": "nDCG@10", "model": "HGN", "row": 11, "task": "Recommendation Systems", "value": "0.113" }, { "column": 7, "dataset": "GoodReads-Comics", "metric": "nDCG@10", "model": "HGN", "row": 12, "task": "Recommendation Systems", "value": "0.1927" } ] } ]

[ { "index": 1, "records": [ { "column": 1, "dataset": "Last.FM", "metric": "HR@10", "model": "Ekar*", "row": 10, "task": "Recommendation Systems", "value": "0.2483" }, { "column": 2, "dataset": "Last.FM", "metric": "nDCG@10", "model": "Ekar*", "row": 10, "task": "Recommendation Systems", "value": "0.1766" }, { "column": 3, "dataset": "MovieLens 1M", "metric": "HR@10", "model": "Ekar*", "row": 10, "task": "Recommendation Systems", "value": "0.1994" }, { "column": 4, "dataset": "MovieLens 1M", "metric": "nDCG@10", "model": "Ekar*", "row": 10, "task": "Recommendation Systems", "value": "0.3699" }, { "column": 5, "dataset": "DBbook2014", "metric": "HR@10", "model": "Ekar*", "row": 10, "task": "Recommendation Systems", "value": "0.1874" }, { "column": 6, "dataset": "DBbook2014", "metric": "nDCG@10", "model": "Ekar*", "row": 10, "task": "Recommendation Systems", "value": "0.1371" } ] } ]

[ { "index": 0, "records": [ { "column": 1, "dataset": "VQA-CP", "metric": "Score", "model": "RUBi", "row": 10, "task": "Visual Question Answering", "value": "47.11" } ] }, { "index": 2, "records": [ { "column": 2, "dataset": "VQA v2 test-dev", "metric": "Accuracy", "model": "RUBi (ours)", "row": 2, "task": "Visual Question Answering", "value": "63.18" } ] } ]

[ { "index": 0, "records": [ { "column": 1, "dataset": "SVHN, 250 Labels", "metric": "Accuracy", "model": "SESEMI SSL (ConvNet)", "row": 11, "task": "Semi-Supervised Image Classification", "value": "91.68" }, { "column": 2, "dataset": "SVHN, 500 Labels", "metric": "Accuracy", "model": "SESEMI SSL (ConvNet)", "row": 11, "task": "Semi-Supervised Image Classification", "value": "93.5" }, { "column": 3, "dataset": "SVHN, 1000 labels", "metric": "Accuracy", "model": "SESEMI SSL (ConvNet)", "row": 11, "task": "Semi-Supervised Image Classification", "value": "94.41" } ] }, { "index": 1, "records": [ { "column": 1, "dataset": "CIFAR-10, 1000 Labels", "metric": "Accuracy", "model": "SESEMI SSL (ConvNet)", "row": 11, "task": "Semi-Supervised Image Classification", "value": "82.12" }, { "column": 2, "dataset": "CIFAR-10, 2000 Labels", "metric": "Accuracy", "model": "SESEMI SSL (ConvNet)", "row": 11, "task": "Semi-Supervised Image Classification", "value": "85.78" }, { "column": 3, "dataset": "CIFAR-10, 4000 Labels", "metric": "Accuracy", "model": "SESEMI SSL (ConvNet)", "row": 11, "task": "Semi-Supervised Image Classification", "value": "88.35" } ] }, { "index": 2, "records": [ { "column": 1, "dataset": "cifar-100, 10000 Labels", "metric": "Accuracy", "model": "SESEMI SSL (ConvNet)", "row": 7, "task": "Semi-Supervised Image Classification", "value": "61.29" } ] } ]

[ { "index": 3, "records": [ { "column": 1, "dataset": "VQA v2 test-dev", "metric": "Accuracy", "model": "MCANed-6", "row": 6, "task": "Visual Question Answering", "value": "70.63" }, { "column": 5, "dataset": "VQA v2 test-std", "metric": "Accuracy", "model": "MCANed-6", "row": 6, "task": "Visual Question Answering", "value": "70.9" } ] } ]

[ { "index": 1, "records": [ { "column": 12, "dataset": "Bitcoin-Alpha", "metric": "Accuracy", "model": "SiGAT", "row": 2, "task": "Link Sign Prediction", "value": "0.9480" }, { "column": 12, "dataset": "Bitcoin-Alpha", "metric": "AUC", "model": "SiGAT", "row": 3, "task": "Link Sign Prediction", "value": "0.9727" }, { "column": 12, "dataset": "Bitcoin-Alpha", "metric": "Macro-F1", "model": "SiGAT", "row": 4, "task": "Link Sign Prediction", "value": "0.7138" }, { "column": 12, "dataset": "Bitcoin-Alpha", "metric": "AUC", "model": "SiGAT", "row": 5, "task": "Link Sign Prediction", "value": "0.8942" }, { "column": 12, "dataset": "Slashdot", "metric": "Accuracy", "model": "SiGAT", "row": 6, "task": "Link Sign Prediction", "value": "0.8482" }, { "column": 12, "dataset": "Slashdot", "metric": "AUC", "model": "SiGAT", "row": 7, "task": "Link Sign Prediction", "value": "0.9047" }, { "column": 12, "dataset": "Slashdot", "metric": "Macro-F1", "model": "SiGAT", "row": 8, "task": "Link Sign Prediction", "value": "0.7659999999999999" }, { "column": 12, "dataset": "Slashdot", "metric": "AUC", "model": "SiGAT", "row": 9, "task": "Link Sign Prediction", "value": "0.8864" }, { "column": 12, "dataset": "Epinions", "metric": "Accuracy", "model": "SiGAT", "row": 10, "task": "Link Sign Prediction", "value": "0.9293" }, { "column": 12, "dataset": "Epinions", "metric": "AUC", "model": "SiGAT", "row": 11, "task": "Link Sign Prediction", "value": "0.9593" }, { "column": 12, "dataset": "Epinions", "metric": "Macro-F1", "model": "SiGAT", "row": 12, "task": "Link Sign Prediction", "value": "0.8449" }, { "column": 12, "dataset": "Epinions", "metric": "AUC", "model": "SiGAT", "row": 13, "task": "Link Sign Prediction", "value": "0.9333" } ] } ]

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