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 ⌀ |
|---|---|---|---|---|---|
Dehazing > Image Dehazing | SOTS Outdoor | DehazeFormer-B | https://arxiv.org/abs/2204.03883v1 | PSNR | 34.95 |
Dehazing > Image Dehazing | SOTS Outdoor | DehazeFormer-B | https://arxiv.org/abs/2204.03883v1 | SSIM | 0.984 |
Dehazing > Image Dehazing | SOTS Outdoor | MAXIM-2S | https://arxiv.org/abs/2201.02973v2 | PSNR | 34.19 |
Dehazing > Image Dehazing | SOTS Outdoor | FFA-Net | https://arxiv.org/abs/1911.07559v2 | PSNR | 33.57 |
Dehazing > Image Dehazing | SOTS Outdoor | FFA-Net | https://arxiv.org/abs/1911.07559v2 | SSIM | 0.9804 |
Dehazing > Image Dehazing | SOTS Outdoor | U2-Former | https://arxiv.org/abs/2112.02279v2 | PSNR | 31.10 |
Dehazing > Image Dehazing | SOTS Outdoor | U2-Former | https://arxiv.org/abs/2112.02279v2 | SSIM | 0.976 |
Dehazing > Image Dehazing | SOTS Outdoor | GridDehazeNet | https://arxiv.org/abs/1908.03245v1 | PSNR | 30.86 |
Dehazing > Image Dehazing | SOTS Outdoor | GridDehazeNet | https://arxiv.org/abs/1908.03245v1 | SSIM | 0.982 |
Dehazing > Image Dehazing | SOTS Outdoor | GMAN | https://arxiv.org/abs/1810.02862v2 | PSNR | 28.19 |
Dehazing > Image Dehazing | SOTS Outdoor | GMAN | https://arxiv.org/abs/1810.02862v2 | SSIM | 0.9638 |
Dehazing > Image Dehazing | SOTS Outdoor | Uformer | https://arxiv.org/abs/2106.03106v2 | PSNR | 26.52 |
Dehazing > Image Dehazing | SOTS Outdoor | Uformer | https://arxiv.org/abs/2106.03106v2 | SSIM | 0.945 |
Dehazing > Image Dehazing | SOTS Outdoor | EMRA-Net | https://link.springer.com/article/10.1007/s11042-021-11081-x | PSNR | 25.81 |
Dehazing > Image Dehazing | SOTS Outdoor | EMRA-Net | https://link.springer.com/article/10.1007/s11042-021-11081-x | SSIM | 0.9409 |
Dehazing > Image Dehazing | SOTS Outdoor | AOD-Net | http://openaccess.thecvf.com/content_iccv_2017/html/Li_AOD-Net_All-In-One_Dehazing_ICCV_2017_paper.html | PSNR | 24.14 |
Dehazing > Image Dehazing | SOTS Outdoor | AOD-Net | http://openaccess.thecvf.com/content_iccv_2017/html/Li_AOD-Net_All-In-One_Dehazing_ICCV_2017_paper.html | SSIM | 0.920 |
Dehazing > Image Dehazing | SOTS Outdoor | Deep DCP | https://arxiv.org/abs/1812.07051v2 | PSNR | 24.08 |
Dehazing > Image Dehazing | SOTS Outdoor | Deep DCP | https://arxiv.org/abs/1812.07051v2 | SSIM | 0.933 |
Dehazing > Image Dehazing | SOTS Outdoor | Deep Energy (Network) | https://arxiv.org/abs/1805.12355v2 | PSNR | 24.07 |
Dehazing > Image Dehazing | SOTS Outdoor | Deep Energy (Network) | https://arxiv.org/abs/1805.12355v2 | SSIM | 0.933 |
Dehazing > Image Dehazing | SOTS Outdoor | EPDN | http://openaccess.thecvf.com/content_CVPR_2019/html/Qu_Enhanced_Pix2pix_Dehazing_Network_CVPR_2019_paper.html | PSNR | 22.57 |
Dehazing > Image Dehazing | SOTS Outdoor | EPDN | http://openaccess.thecvf.com/content_CVPR_2019/html/Qu_Enhanced_Pix2pix_Dehazing_Network_CVPR_2019_paper.html | SSIM | 0.8630 |
Dehazing > Image Dehazing | SOTS Outdoor | GFN | http://arxiv.org/abs/1804.00213v1 | PSNR | 22.30 |
Dehazing > Image Dehazing | SOTS Outdoor | GFN | http://arxiv.org/abs/1804.00213v1 | SSIM | 0.880 |
Dehazing > Single Image Dehazing | RESIDE | Lower Bound on Transmission using Non-Linear Bounding Function in Single Image Dehazing | https://ieeexplore.ieee.org/document/9018379 | SSIM | 0.88 |
Dehazing > Single Image Dehazing | RESIDE | Lower Bound on Transmission using Non-Linear Bounding Function in Single Image Dehazing | https://ieeexplore.ieee.org/document/9018379 | Average PSNR | 20.01 |
Dehazing > Single Image Dehazing | UIEB | Bradley-Terry model | https://ieeexplore.ieee.org/document/9201388 | L2 Norm | minimum is better |
Dehazing > Single Image Dehazing | NH-HAZE | DehazeDCT | https://github.com/movingforward100/Dehazing_R | PSNR | 22.78 |
Dehazing > Single Image Dehazing | NH-HAZE2 | DehazeDCT | https://github.com/movingforward100/Dehazing_R | PSNR | 22.86 |
Dehazing > Single Image Dehazing | NH-HAZE2 | DehazeDCT | https://github.com/movingforward100/Dehazing_R | SSIM | 0.877 |
Dehazing > Single Image Dehazing | HD-NH-HAZE | DehazeDCT | https://github.com/movingforward100/Dehazing_R | PSNR | 22.36 |
Dehazing > Single Image Dehazing | HD-NH-HAZE | DehazeDCT | https://github.com/movingforward100/Dehazing_R | SSIM | 0.752 |
Dehazing > Single Image Dehazing | DNH-HAZE | DehazeDCT | https://github.com/movingforward100/Dehazing_R | PSNR | 21.73 |
Dehazing > Single Image Dehazing | DNH-HAZE | DehazeDCT | https://github.com/movingforward100/Dehazing_R | SSIM | 0.743 |
Machine Translation | WMT2017 English-French | OmniNetP | https://arxiv.org/abs/2103.01075v1 | BLEU | 43.1 |
Machine Translation | WMT2019 Finnish-English | CT+B/S construction | https://arxiv.org/abs/1907.00494v1 | BLEU | 34.1 |
Machine Translation | IWSLT2015 German-English | PS-KD | https://arxiv.org/abs/2006.12000v3 | BLEU score | 36.20 |
Machine Translation | IWSLT2015 German-English | Pervasive Attention | http://arxiv.org/abs/1808.03867v3 | BLEU score | 34.18 |
Machine Translation | IWSLT2015 German-English | Transformer with FRAGE | https://arxiv.org/abs/1809.06858v2 | BLEU score | 33.97 |
Machine Translation | IWSLT2015 German-English | ConvS2S+Risk | http://arxiv.org/abs/1711.04956v5 | BLEU score | 32.93 |
Machine Translation | IWSLT2015 German-English | Denoising autoencoders (non-autoregressive) | http://arxiv.org/abs/1802.06901v3 | BLEU score | 32.43 |
Machine Translation | IWSLT2015 German-English | ConvS2S | http://arxiv.org/abs/1705.03122v3 | BLEU score | 32.31 |
Machine Translation | IWSLT2015 German-English | Conv-LSTM (deep+pos) | http://arxiv.org/abs/1611.02344v3 | BLEU score | 30.4 |
Machine Translation | IWSLT2015 German-English | NPMT + language model | http://arxiv.org/abs/1706.05565v8 | BLEU score | 30.08 |
Machine Translation | IWSLT2015 German-English | RNNsearch | http://arxiv.org/abs/1607.07086v3 | BLEU score | 29.98 |
Machine Translation | IWSLT2015 German-English | DCCL | http://arxiv.org/abs/1711.01068v2 | BLEU score | 29.56 |
Machine Translation | IWSLT2015 German-English | Bi-GRU (MLE+SLE) | http://arxiv.org/abs/1409.0473v7 | BLEU score | 28.53 |
Machine Translation | IWSLT2015 German-English | FlowSeq-base | https://arxiv.org/abs/1909.02480v3 | BLEU score | 24.75 |
Machine Translation | IWSLT2015 German-English | Word-level CNN w/attn, input feeding | http://arxiv.org/abs/1606.02960v2 | BLEU score | 24.0 |
Machine Translation | IWSLT2015 German-English | Word-level LSTM w/attn | http://arxiv.org/abs/1511.06732v7 | BLEU score | 20.2 |
Machine Translation | IWSLT2015 German-English | QRNN | http://arxiv.org/abs/1611.01576v2 | BLEU score | 19.41 |
Machine Translation | WMT2016 English-Czech | Attentional encoder-decoder + BPE | http://arxiv.org/abs/1606.02891v2 | BLEU score | 25.8 |
Machine Translation | IWSLT2015 Vietnamese-English | HeadMask (Random-18) | https://arxiv.org/abs/2009.09672v2 | BLEU | 26.85 |
Machine Translation | IWSLT2015 Vietnamese-English | HeadMask (Impt-18) | https://arxiv.org/abs/2009.09672v2 | BLEU | 26.36 |
Machine Translation | IWSLT2017 French-English | Transformer base + BPE-Dropout | https://arxiv.org/abs/1910.13267v2 | Cased sacreBLEU | 38.6 |
Machine Translation | IWSLT2017 French-English | NLLB-200 | https://arxiv.org/abs/2207.04672v3 | SacreBLEU | 45.8 |
Machine Translation | WMT 2018 English-Estonian | Multi-pass backtranslated adapted transformer | https://aclanthology.org/W18-6423 | BLEU | 24.10 |
Machine Translation | WMT2015 English-German | ByteNet | http://arxiv.org/abs/1610.10099v2 | BLEU score | 26.3 |
Machine Translation | WMT2015 English-German | S2Tree+5gram NPLM | null | BLEU score | 24.1 |
Machine Translation | WMT2015 English-German | Enc-Dec Att (char) | http://arxiv.org/abs/1603.06147v4 | BLEU score | 23.5 |
Machine Translation | WMT2015 English-German | BPE word segmentation | http://arxiv.org/abs/1508.07909v5 | BLEU score | 22.8 |
Machine Translation | WMT2015 English-German | Enc-Dec Att (BPE) | http://arxiv.org/abs/1603.06147v4 | BLEU score | 21.7 |
Machine Translation | WMT2015 English-German | Unsupervised attentional encoder-decoder + BPE | http://arxiv.org/abs/1710.11041v2 | BLEU score | 6.89 |
Machine Translation | WMT2017 English-German | OmniNetP | https://arxiv.org/abs/2103.01075v1 | BLEU | 29.0 |
Machine Translation | IWSLT2015 English-German | PS-KD | https://arxiv.org/abs/2006.12000v3 | BLEU score | 30.00 |
Machine Translation | IWSLT2015 English-German | Transformer | https://arxiv.org/abs/1706.03762v7 | BLEU score | 28.50 |
Machine Translation | IWSLT2015 English-German | NAT +FT + NPD | http://arxiv.org/abs/1711.02281v2 | BLEU score | 28.16 |
Machine Translation | IWSLT2015 English-German | Pervasive Attention | http://arxiv.org/abs/1808.03867v3 | BLEU score | 27.99 |
Machine Translation | IWSLT2015 English-German | Denoising autoencoders (non-autoregressive) | http://arxiv.org/abs/1802.06901v3 | BLEU score | 27.01 |
Machine Translation | IWSLT2015 English-German | ConvS2S | http://arxiv.org/abs/1705.03122v3 | BLEU score | 26.73 |
Machine Translation | IWSLT2015 English-German | NPMT + language model | http://arxiv.org/abs/1706.05565v8 | BLEU score | 25.36 |
Machine Translation | IWSLT2015 English-German | RNNsearch | http://arxiv.org/abs/1607.07086v3 | BLEU score | 25.04 |
Machine Translation | V_B (trained on T_H) | M_C | https://arxiv.org/abs/2102.06320v1 | Median Relative Edit Distance | 0.25 |
Machine Translation | WMT 2022 Czech-English | Vega-MT | https://arxiv.org/abs/2209.09444v4 | SacreBLEU | 54.9 |
Machine Translation | WMT2016 Romanian-English | fast-noisy-channel-modeling | https://arxiv.org/abs/2011.07164v1 | BLEU score | 40.3 |
Machine Translation | WMT2016 Romanian-English | FLAN 137B (few-shot, k=9) | https://arxiv.org/abs/2109.01652v5 | BLEU score | 38.1 |
Machine Translation | WMT2016 Romanian-English | FLAN 137B (zero-shot) | https://arxiv.org/abs/2109.01652v5 | BLEU score | 37.3 |
Machine Translation | WMT2016 Romanian-English | MLM pretraining | http://arxiv.org/abs/1901.07291v1 | BLEU score | 35.3 |
Machine Translation | WMT2016 Romanian-English | GenTranslate | https://arxiv.org/abs/2402.06894v2 | BLEU score | 33.5 |
Machine Translation | WMT2016 Romanian-English | Attentional encoder-decoder + BPE | http://arxiv.org/abs/1606.02891v2 | BLEU score | 33.3 |
Machine Translation | WMT2016 Romanian-English | Levenshtein Transformer (distillation) | https://arxiv.org/abs/1905.11006v2 | BLEU score | 33.26 |
Machine Translation | WMT2016 Romanian-English | CMLM+LAT+4 iterations | https://arxiv.org/abs/2011.06132v1 | BLEU score | 33.26 |
Machine Translation | WMT2016 Romanian-English | Adaptively Sparse Transformer (1.5-entmax) | https://arxiv.org/abs/1909.00015v2 | BLEU score | 33.1 |
Machine Translation | WMT2016 Romanian-English | HeadMask (Impt-18) | https://arxiv.org/abs/2009.09672v2 | BLEU score | 32.95 |
Machine Translation | WMT2016 Romanian-English | FlowSeq-large (NPD n = 30) | https://arxiv.org/abs/1909.02480v3 | BLEU score | 32.91 |
Machine Translation | WMT2016 Romanian-English | Adaptively Sparse Transformer (alpha-entmax) | https://arxiv.org/abs/1909.00015v2 | BLEU score | 32.89 |
Machine Translation | WMT2016 Romanian-English | HeadMask (Random-18) | https://arxiv.org/abs/2009.09672v2 | BLEU score | 32.85 |
Machine Translation | WMT2016 Romanian-English | FlowSeq-large (NPD n = 15) | https://arxiv.org/abs/1909.02480v3 | BLEU score | 32.46 |
Machine Translation | WMT2016 Romanian-English | FlowSeq-large (IWD n = 15) | https://arxiv.org/abs/1909.02480v3 | BLEU score | 32.03 |
Machine Translation | WMT2016 Romanian-English | NAT +FT + NPD | http://arxiv.org/abs/1711.02281v2 | BLEU score | 31.44 |
Machine Translation | WMT2016 Romanian-English | CMLM+LAT+1 iterations | https://arxiv.org/abs/2011.06132v1 | BLEU score | 31.24 |
Machine Translation | WMT2016 Romanian-English | FlowSeq-large | https://arxiv.org/abs/1909.02480v3 | BLEU score | 30.69 |
Machine Translation | WMT2016 Romanian-English | Denoising autoencoders (non-autoregressive) | http://arxiv.org/abs/1802.06901v3 | BLEU score | 30.30 |
Machine Translation | WMT2016 Romanian-English | FlowSeq-base | https://arxiv.org/abs/1909.02480v3 | BLEU score | 30.16 |
Machine Translation | WMT2016 Romanian-English | BART (TextBox 2.0) | https://arxiv.org/abs/2212.13005v1 | BLEU-4 | 37.48 |
Machine Translation | V_A (trained on T_H) | M_C | https://arxiv.org/abs/2102.06320v1 | Median Relative Edit Distance | 0.28 |
Machine Translation | WMT 2017 English-Latvian | Transformer trained on highly filtered data | http://arxiv.org/abs/1810.08392v1 | BLEU | 22.89 |
Machine Translation | WMT2019 English-Japanese | fiore | https://arxiv.org/abs/2005.06166v1 | BLEU | 527424878 |
Machine Translation | WMT 2022 Chinese-English | Vega-MT | https://arxiv.org/abs/2209.09444v4 | SacreBLEU | 33.5 |
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