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 > Partial Domain Adaptation | Office-Home | ETN | http://arxiv.org/abs/1903.12230v2 | Accuracy (%) | 70.5 |
Domain Adaptation > Partial Domain Adaptation | Office-31 | SLM | https://arxiv.org/abs/2012.03358v2 | Accuracy (%) | 98.38 |
Domain Adaptation > Partial Domain Adaptation | Office-31 | SCS-LP | https://breckon.org/toby/publications/papers/wang21pda.pdf | Accuracy (%) | 98.1 |
Domain Adaptation > Partial Domain Adaptation | Office-31 | SPDA | https://bmvc2022.mpi-inf.mpg.de/0420.pdf | Accuracy (%) | 98.01 |
Domain Adaptation > Partial Domain Adaptation | Office-31 | DCC | http://www.guangrui.li/papers/guangruiCVPR2021.pdf | Accuracy (%) | 97.90 |
Domain Adaptation > Partial Domain Adaptation | Office-31 | BA^3US | https://arxiv.org/abs/2003.02541v2 | Accuracy (%) | 97.8 |
Domain Adaptation > Partial Domain Adaptation | Office-31 | RTNet_adv | https://arxiv.org/abs/1905.10756v4 | Accuracy (%) | 96.9 |
Domain Adaptation > Partial Domain Adaptation | Office-31 | ETN | http://arxiv.org/abs/1903.12230v2 | Accuracy (%) | 96.7 |
Domain Adaptation > Partial Domain Adaptation | ImageNet-Caltech | AR | http://proceedings.neurips.cc/paper/2021/hash/7ce3284b743aefde80ffd9aec500e085-Abstract.html | Accuracy (%) | 84.69 |
Domain Adaptation > Partial Domain Adaptation | ImageNet-Caltech | BA^3US | https://arxiv.org/abs/2003.02541v2 | Accuracy (%) | 83.7 |
Domain Adaptation > Partial Domain Adaptation | ImageNet-Caltech | SLM | https://arxiv.org/abs/2012.03358v2 | Accuracy (%) | 81.86 |
Domain Adaptation > Partial Domain Adaptation | ImageNet-Caltech | ETN | http://arxiv.org/abs/1903.12230v2 | Accuracy (%) | 79.1 |
Domain Adaptation > Partial Domain Adaptation | VisDA2017 | SPDA | https://bmvc2022.mpi-inf.mpg.de/0420.pdf | Accuracy (%) | 87.69 |
Domain Adaptation > Partial Domain Adaptation | VisDA2017 | SLM | https://arxiv.org/abs/2012.03358v2 | Accuracy (%) | 84.61 |
Domain Adaptation > Partial Domain Adaptation | VisDA2017 | AR | http://proceedings.neurips.cc/paper/2021/hash/7ce3284b743aefde80ffd9aec500e085-Abstract.html | Accuracy (%) | 83.62 |
Domain Adaptation > Partial Domain Adaptation | DomainNet | AR | http://proceedings.neurips.cc/paper/2021/hash/7ce3284b743aefde80ffd9aec500e085-Abstract.html | Accuracy (%) | 65.76 |
Domain Adaptation > Partial Domain Adaptation | DomainNet | BA^3US | https://arxiv.org/abs/2003.02541v2 | Accuracy (%) | 60.63 |
Domain Adaptation > Partial Domain Adaptation | DomainNet | PADA | http://arxiv.org/abs/1808.04205v1 | Accuracy (%) | 37.41 |
Domain Adaptation > Continuously Indexed Domain Adaptation | Sine | CIDA | https://arxiv.org/abs/2007.01807v2 | Accuracy (%) | 95% |
Domain Adaptation > Continuously Indexed Domain Adaptation | Indexed Rotating MNIST | PCIDA | https://arxiv.org/abs/2007.01807v2 | Accuracy (%) | 87.1% |
Domain Adaptation > Continuously Indexed Domain Adaptation | Indexed Rotating MNIST | CIDA | https://arxiv.org/abs/2007.01807v2 | Accuracy (%) | 85.7% |
Domain Adaptation > Continuously Indexed Domain Adaptation | Circle | CIDA | https://arxiv.org/abs/2007.01807v2 | Accuracy (%) | 94% |
Domain Adaptation > Blended-target Domain Adaptation | DomainNet | MCDA | https://arxiv.org/abs/2302.01516v2 | Average Accuracy | 34.5 |
Domain Adaptation > Blended-target Domain Adaptation | DomainNet | CGCT | https://arxiv.org/abs/2104.00808v1 | Average Accuracy | 32.3 |
Domain Adaptation > Blended-target Domain Adaptation | Office-Home | MCDA | https://arxiv.org/abs/2302.01516v2 | Average Accuracy | 71.1 |
Domain Adaptation > Blended-target Domain Adaptation | Office-Home | CGCT | https://arxiv.org/abs/2104.00808v1 | Average Accuracy | 66.5 |
Domain Adaptation > Blended-target Domain Adaptation | Office-31 | MCDA | https://arxiv.org/abs/2302.01516v2 | Average Accuracy | 89.6 |
Domain Adaptation > Blended-target Domain Adaptation | Office-31 | DCGCT | https://arxiv.org/abs/2104.00808v1 | Average Accuracy | 88.2 |
Domain Adaptation > Wildly Unsupervised Domain Adaptation | Noisy-Amazon (20%) | Butterfly | https://arxiv.org/abs/1905.07720v3 | Average Accuracy | 71.53 |
Domain Adaptation > Wildly Unsupervised Domain Adaptation | Noisy-SYND-to-MNIST | Butterfly | https://arxiv.org/abs/1905.07720v3 | Average Accuracy | 94.09 |
Domain Adaptation > Wildly Unsupervised Domain Adaptation | Noisy-MNIST-to-SYND | Butterfly | https://arxiv.org/abs/1905.07720v3 | Average Accuracy | 57.55 |
Domain Adaptation > Wildly Unsupervised Domain Adaptation | Noisy-Amazon (45%) | Butterfly | https://arxiv.org/abs/1905.07720v3 | Average Accuracy | 56.01 |
Domain Adaptation > Open-Set Multi-Target Domain Adaptation | MiniDomainNet | COSMo | https://arxiv.org/abs/2409.00397v2 | H-Score | 81.84 |
Domain Adaptation > Open-Set Multi-Target Domain Adaptation | Office-Home | COSMo | https://arxiv.org/abs/2409.00397v2 | H-Score | 83.04 |
Domain Adaptation > Open-Set Multi-Target Domain Adaptation | Office-31 | COSMo | https://arxiv.org/abs/2409.00397v2 | H-Score | 90.01 |
Speech Recognition | LRS3-TED | Whisper | https://arxiv.org/abs/2406.10082v3 | Word Error Rate (WER) | 0.68 |
Speech Recognition | LRS3-TED | Llama-AVSR | https://arxiv.org/abs/2409.12319v2 | Word Error Rate (WER) | 0.81 |
Speech Recognition | LRS3-TED | AV-HuBERT Large | https://arxiv.org/abs/2201.02184v2 | Word Error Rate (WER) | 1.3 |
Speech Recognition | LRS3-TED | RAVEn Large | https://arxiv.org/abs/2212.06246v2 | Word Error Rate (WER) | 1.4 |
Speech Recognition | Libri-Light test-other | wav2vec 2.0 Large-10h-LV-60k | https://arxiv.org/abs/2006.11477v3 | Word Error Rate (WER) | 5.0 |
Speech Recognition | Libri-Light test-other | TDS 60k pseudo-label + CTC fine-tuning + 4gram-LM | https://arxiv.org/abs/1912.07875v1 | Word Error Rate (WER) | 56.6 |
Speech Recognition | Libri-Light test-other | CPC unlab-60k+train-10h CPC pretrain + CTC fine-tuning + 4gram-LM | https://arxiv.org/abs/1912.07875v1 | Word Error Rate (WER) | 69.5 |
Speech Recognition | Libri-Light test-other | CPC unlab-60k | https://arxiv.org/abs/1912.07875v1 | ABX-within | 8.14 |
Speech Recognition | Libri-Light test-other | CPC unlab-60k | https://arxiv.org/abs/1912.07875v1 | ABX-across | 13.42 |
Speech Recognition | Libri-Light test-other | S6000h-n42-τ2 → 0.1 | https://arxiv.org/abs/2005.14578v1 | ABX-within | 12.05 |
Speech Recognition | Libri-Light test-other | S6000h-n42-τ2 → 0.1 | https://arxiv.org/abs/2005.14578v1 | ABX-across | 20.6 |
Speech Recognition | WSJ dev93 | CTC-CRF ST-NAS | https://arxiv.org/abs/2011.05649v1 | Word Error Rate (WER) | 5.68 |
Speech Recognition | WSJ dev93 | CTC-CRF VGG-BLSTM | https://arxiv.org/abs/2005.13326v2 | Word Error Rate (WER) | 5.7 |
Speech Recognition | WSJ dev93 | Convolutional Speech Recognition | https://ieeexplore.ieee.org/document/8682256 | Word Error Rate (WER) | 6.23 |
Speech Recognition | WSJ dev93 | Convolutional Speech Recognition | http://arxiv.org/abs/1812.06864v2 | Word Error Rate (WER) | 6.8 |
Speech Recognition | Switchboard CallHome | SpeechStew (100M) | https://arxiv.org/abs/2104.02133v3 | Word Error Rate (WER) | 8.3 |
Speech Recognition | facebook/multilingual_librispeech german | TDT 0-4 | https://arxiv.org/abs/2304.06795v2 | WER | 3.93 |
Speech Recognition | AMI IMH | ConformerXXL-P + Downstream NST | https://arxiv.org/abs/2109.13226v3 | Word Error Rate (WER) | 7.8 |
Speech Recognition | AMI IMH | SpeechStew (100M) | https://arxiv.org/abs/2104.02133v3 | Word Error Rate (WER) | 9 |
Speech Recognition | AISHELL-2 Test Android | Qwen-Audio | https://arxiv.org/abs/2311.07919v2 | Word Error Rate (WER) | 3.3 |
Speech Recognition | CAS-VSR-S101 | ES³ Base* | http://openaccess.thecvf.com//content/CVPR2024/html/Zhang_ES3_Evolving_Self-Supervised_Learning_of_Robust_Audio-Visual_Speech_Representations_CVPR_2024_paper.html | Word Error Rate (WER) | 11.6 |
Speech Recognition | SLUE | W2V2-L-LL60K (+ TED-LIUM 3 LM) | https://arxiv.org/abs/2111.10367v3 | VoxPopuli (Dev) | 9.1 |
Speech Recognition | SLUE | W2V2-L-LL60K (+ TED-LIUM 3 LM) | https://arxiv.org/abs/2111.10367v3 | VoxPopuli (Test) | 9.3 |
Speech Recognition | SLUE | W2V2-L-LL60K (+ TED-LIUM 3 LM) | https://arxiv.org/abs/2111.10367v3 | VoxCeleb (Dev) | 9.1 |
Speech Recognition | SLUE | W2V2-L-LL60K (+ TED-LIUM 3 LM) | https://arxiv.org/abs/2111.10367v3 | VoxCeleb (Test) | 10.8 |
Speech Recognition | SLUE | W2V2-B-LS960 (+ TED-LIUM 3 LM) | https://arxiv.org/abs/2111.10367v3 | VoxPopuli (Dev) | 12.0 |
Speech Recognition | SLUE | W2V2-B-LS960 (+ TED-LIUM 3 LM) | https://arxiv.org/abs/2111.10367v3 | VoxPopuli (Test) | 12.2 |
Speech Recognition | SLUE | W2V2-B-LS960 (+ TED-LIUM 3 LM) | https://arxiv.org/abs/2111.10367v3 | VoxCeleb (Dev) | 13.2 |
Speech Recognition | SLUE | W2V2-B-LS960 (+ TED-LIUM 3 LM) | https://arxiv.org/abs/2111.10367v3 | VoxCeleb (Test) | 15.8 |
Speech Recognition | SLUE | W2V2-L-LL60K (+ in-domain LM) | https://arxiv.org/abs/2111.10367v3 | VoxPopuli (Dev) | 12.0 |
Speech Recognition | SLUE | W2V2-L-LL60K (+ in-domain LM) | https://arxiv.org/abs/2111.10367v3 | VoxPopuli (Test) | 12.5 |
Speech Recognition | SLUE | W2V2-L-LL60K (+ in-domain LM) | https://arxiv.org/abs/2111.10367v3 | VoxCeleb (Dev) | 11.8 |
Speech Recognition | SLUE | W2V2-L-LL60K (+ in-domain LM) | https://arxiv.org/abs/2111.10367v3 | VoxCeleb (Test) | 13.8 |
Speech Recognition | SLUE | W2V2-L-LL60K | https://arxiv.org/abs/2111.10367v3 | VoxPopuli (Dev) | 14.0 |
Speech Recognition | SLUE | W2V2-L-LL60K | https://arxiv.org/abs/2111.10367v3 | VoxPopuli (Test) | 12.1 |
Speech Recognition | SLUE | W2V2-L-LL60K | https://arxiv.org/abs/2111.10367v3 | VoxCeleb (Dev) | 11.0 |
Speech Recognition | SLUE | W2V2-L-LL60K | https://arxiv.org/abs/2111.10367v3 | VoxCeleb (Test) | 13.5 |
Speech Recognition | SLUE | W2V2-B-LS960 (+ in-domain LM) | https://arxiv.org/abs/2111.10367v3 | VoxPopuli (Dev) | 14.6 |
Speech Recognition | SLUE | W2V2-B-LS960 (+ in-domain LM) | https://arxiv.org/abs/2111.10367v3 | VoxPopuli (Test) | 15.2 |
Speech Recognition | SLUE | W2V2-B-LS960 (+ in-domain LM) | https://arxiv.org/abs/2111.10367v3 | VoxCeleb (Dev) | 15.2 |
Speech Recognition | SLUE | W2V2-B-LS960 (+ in-domain LM) | https://arxiv.org/abs/2111.10367v3 | VoxCeleb (Test) | 18.2 |
Speech Recognition | SLUE | W2V2-B-LS960 | https://arxiv.org/abs/2111.10367v3 | VoxPopuli (Dev) | 17.2 |
Speech Recognition | SLUE | W2V2-B-LS960 | https://arxiv.org/abs/2111.10367v3 | VoxPopuli (Test) | 17.9 |
Speech Recognition | SLUE | W2V2-B-LS960 | https://arxiv.org/abs/2111.10367v3 | VoxCeleb (Dev) | 17.2 |
Speech Recognition | SLUE | W2V2-B-LS960 | https://arxiv.org/abs/2111.10367v3 | VoxCeleb (Test) | 20.5 |
Speech Recognition | SLUE | HuBERT-B-LS960 | https://arxiv.org/abs/2111.10367v3 | VoxPopuli (Dev) | 18.6 |
Speech Recognition | SLUE | HuBERT-B-LS960 | https://arxiv.org/abs/2111.10367v3 | VoxPopuli (Test) | 19.1 |
Speech Recognition | SLUE | HuBERT-B-LS960 | https://arxiv.org/abs/2111.10367v3 | VoxCeleb (Dev) | 19.6 |
Speech Recognition | SLUE | HuBERT-B-LS960 | https://arxiv.org/abs/2111.10367v3 | VoxCeleb (Test) | 21.2 |
Speech Recognition | SLUE | W2V2-B-VP100K | https://arxiv.org/abs/2111.10367v3 | VoxPopuli (Dev) | 21.6 |
Speech Recognition | SLUE | W2V2-B-VP100K | https://arxiv.org/abs/2111.10367v3 | VoxPopuli (Test) | 22.4 |
Speech Recognition | SLUE | W2V2-B-VP100K | https://arxiv.org/abs/2111.10367v3 | VoxCeleb (Dev) | 29.9 |
Speech Recognition | SLUE | W2V2-B-VP100K | https://arxiv.org/abs/2111.10367v3 | VoxCeleb (Test) | 33.4 |
Speech Recognition | AISHELL-2 | Paraformer-large | https://arxiv.org/abs/2305.11013v1 | Word Error Rate (WER) | 2.85 |
Speech Recognition | AISHELL-2 | Paraformer | https://arxiv.org/abs/2305.11013v1 | Word Error Rate (WER) | 5.73 |
Speech Recognition | GigaSpeech DEV | SAMBA ASR | https://arxiv.org/abs/2501.02832v3 | Word Error Rate (WER) | 9.12 |
Speech Recognition | GigaSpeech DEV | Zipformer+pruned transducer w/ CR-CTC
(no external language model) | https://arxiv.org/abs/2410.05101v4 | Word Error Rate (WER) | 9.95 |
Speech Recognition | GigaSpeech DEV | Zipformer+pruned transducer
(no external language model) | https://arxiv.org/abs/2410.05101v4 | Word Error Rate (WER) | 10.09 |
Speech Recognition | GigaSpeech DEV | Zipformer+CR-CTC
(no external language model) | https://arxiv.org/abs/2410.05101v4 | Word Error Rate (WER) | 10.15 |
Speech Recognition | GigaSpeech DEV | Conformer/Transformer-AED | https://arxiv.org/abs/2106.06909v1 | Word Error Rate (WER) | 10.90 |
Speech Recognition | Tedlium | United-MedASR (764M) | https://arxiv.org/abs/2412.00055v1 | Word Error Rate (WER) | 0.29 |
Speech Recognition | Tedlium | parakeet-rnnt-1.1b | https://arxiv.org/abs/2305.05084v6 | Word Error Rate (WER) | 3.92 |
Speech Recognition | Tedlium | Whispering-LLaMa-7b | https://arxiv.org/abs/2309.15701v2 | Word Error Rate (WER) | 4.6 |
Speech Recognition | Tedlium | SpeechStew (100M) | https://arxiv.org/abs/2104.02133v3 | Word Error Rate (WER) | 5.3 |
Speech Recognition | CHiME-6 eval | ConformerXXL-PS + G-Augment | https://arxiv.org/abs/2210.10879v2 | Word Error Rate (WER) | 30.7 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.