felixleungsc/paperswithcode-data-evaluation-tables

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 ⌀
Super-Resolution > Image Rescaling	DIV2K val-q30-4x	T-SAIN	https://arxiv.org/abs/2412.13508v1	PSNR	28.08
Super-Resolution > Image Rescaling	DIV2K val-q30-4x	T-SAIN	https://arxiv.org/abs/2412.13508v1	SSIM	0.7893
Super-Resolution > Image Rescaling	DIV2K val-q30-4x	SAIN	https://arxiv.org/abs/2303.02353v2	PSNR	27.90
Super-Resolution > Image Rescaling	DIV2K val-q30-4x	SAIN	https://arxiv.org/abs/2303.02353v2	SSIM	0.7745
Super-Resolution > Image Rescaling	DIV2K val-q30-4x	IRN	https://arxiv.org/abs/2005.05650v1	PSNR	25.98
Super-Resolution > Image Rescaling	DIV2K val-q30-4x	IRN	https://arxiv.org/abs/2005.05650v1	SSIM	0.6867
Super-Resolution > Image Rescaling	DIV2K val-q30-4x	HCFlow	https://arxiv.org/abs/2108.05301v1	PSNR	25.89
Super-Resolution > Image Rescaling	DIV2K val-q30-4x	HCFlow	https://arxiv.org/abs/2108.05301v1	SSIM	0.6838
Super-Resolution > Image Rescaling	BSD100-4x	T-IRN	https://arxiv.org/abs/2412.13508v1	PSNR	31.64
Super-Resolution > Image Rescaling	BSD100-4x	T-IRN	https://arxiv.org/abs/2412.13508v1	SSIM	0.8837
Super-Resolution > Image Rescaling	BSD100-4x	IRN	https://arxiv.org/abs/2005.05650v1	PSNR	31.64
Super-Resolution > Image Rescaling	BSD100-4x	IRN	https://arxiv.org/abs/2005.05650v1	SSIM	0.8826
Super-Resolution > Image Rescaling	DIV2K val-4x	T-IRN	https://arxiv.org/abs/2412.13508v1	PSNR	35.10
Super-Resolution > Image Rescaling	DIV2K val-4x	T-IRN	https://arxiv.org/abs/2412.13508v1	SSIM	0.9328
Super-Resolution > Image Rescaling	DIV2K val-4x	IRN	https://arxiv.org/abs/2005.05650v1	PSNR	35.07
Super-Resolution > Image Rescaling	DIV2K val-4x	IRN	https://arxiv.org/abs/2005.05650v1	SSIM	0.9318
Super-Resolution > Image Rescaling	Set14-4x	T-IRN	https://arxiv.org/abs/2412.13508v1	PSNR	32.70
Super-Resolution > Image Rescaling	Set14-4x	T-IRN	https://arxiv.org/abs/2412.13508v1	SSIM	0.9003
Super-Resolution > Image Rescaling	Set14-4x	IRN	https://arxiv.org/abs/2005.05650v1	PSNR	32.67
Super-Resolution > Image Rescaling	Set14-4x	IRN	https://arxiv.org/abs/2005.05650v1	SSIM	0.9015
Super-Resolution > Image Rescaling	DIV2K val-q90-4x	T-SAIN	https://arxiv.org/abs/2412.13508v1	PSNR	30.92
Super-Resolution > Image Rescaling	DIV2K val-q90-4x	T-SAIN	https://arxiv.org/abs/2412.13508v1	SSIM	0.8517
Super-Resolution > Image Rescaling	DIV2K val-q90-4x	SAIN	https://arxiv.org/abs/2303.02353v2	PSNR	30.31
Super-Resolution > Image Rescaling	DIV2K val-q90-4x	SAIN	https://arxiv.org/abs/2303.02353v2	SSIM	0.8367
Super-Resolution > Image Rescaling	DIV2K val-q90-4x	IRN	https://arxiv.org/abs/2005.05650v1	PSNR	28.42
Super-Resolution > Image Rescaling	DIV2K val-q90-4x	IRN	https://arxiv.org/abs/2005.05650v1	SSIM	0.7777
Super-Resolution > Image Rescaling	DIV2K val-q90-4x	HCFlow	https://arxiv.org/abs/2108.05301v1	PSNR	27.41
Super-Resolution > Image Rescaling	DIV2K val-q90-4x	HCFlow	https://arxiv.org/abs/2108.05301v1	SSIM	0.7485
Super-Resolution > Image Rescaling	Urban100-4x	IRN	https://arxiv.org/abs/2005.05650v1	PSNR	31.41
Super-Resolution > Image Rescaling	Urban100-4x	IRN	https://arxiv.org/abs/2005.05650v1	SSIM	0.9157
Super-Resolution > Image Rescaling	Urban100-4x	T-IRN	https://arxiv.org/abs/2412.13508v1	PSNR	31.19
Super-Resolution > Image Rescaling	Urban100-4x	T-IRN	https://arxiv.org/abs/2412.13508v1	SSIM	0.9132
Super-Resolution > Image Rescaling	Set14-2x	T-IRN	https://arxiv.org/abs/2412.13508v1	PSNR	41.70
Super-Resolution > Image Rescaling	Set14-2x	T-IRN	https://arxiv.org/abs/2412.13508v1	SSIM	0.9809
Super-Resolution > Image Rescaling	Set14-2x	IRN	https://arxiv.org/abs/2005.05650v1	PSNR	40.79
Super-Resolution > Image Rescaling	Set14-2x	IRN	https://arxiv.org/abs/2005.05650v1	SSIM	0.9778
Super-Resolution > Image Rescaling	DIV2K val-q50-2x	T-SAIN	https://arxiv.org/abs/2412.13508v1	PSNR	33.71
Super-Resolution > Image Rescaling	DIV2K val-q50-2x	T-SAIN	https://arxiv.org/abs/2412.13508v1	SSIM	0.9210
Super-Resolution > Image Rescaling	DIV2K val-q50-2x	SAIN	https://arxiv.org/abs/2303.02353v2	PSNR	33.17
Super-Resolution > Image Rescaling	DIV2K val-q50-2x	SAIN	https://arxiv.org/abs/2303.02353v2	SSIM	0.9082
Super-Resolution > Image Rescaling	DIV2K val-q50-2x	IRN	https://arxiv.org/abs/2005.05650v1	PSNR	30.20
Super-Resolution > Image Rescaling	DIV2K val-q50-2x	IRN	https://arxiv.org/abs/2005.05650v1	SSIM	0.8342
Super-Resolution > Image Rescaling	DIV2K val-q50-4x	T-SAIN	https://arxiv.org/abs/2412.13508v1	PSNR	29.43
Super-Resolution > Image Rescaling	DIV2K val-q50-4x	T-SAIN	https://arxiv.org/abs/2412.13508v1	SSIM	0.8237
Super-Resolution > Image Rescaling	DIV2K val-q50-4x	SAIN	https://arxiv.org/abs/2303.02353v2	PSNR	29.05
Super-Resolution > Image Rescaling	DIV2K val-q50-4x	SAIN	https://arxiv.org/abs/2303.02353v2	SSIM	0.8088
Super-Resolution > Image Rescaling	DIV2K val-q50-4x	IRN	https://arxiv.org/abs/2005.05650v1	PSNR	26.62
Super-Resolution > Image Rescaling	DIV2K val-q50-4x	IRN	https://arxiv.org/abs/2005.05650v1	SSIM	0.7096
Super-Resolution > Image Rescaling	DIV2K val-q50-4x	HCFlow	https://arxiv.org/abs/2108.05301v1	PSNR	26.38
Super-Resolution > Image Rescaling	DIV2K val-q50-4x	HCFlow	https://arxiv.org/abs/2108.05301v1	SSIM	0.7029
Super-Resolution > Reference-based Video Super-Resolution	RealMCVSR	RefVSR-IR-ℓ1	https://arxiv.org/abs/2203.14537v1	PSNR	34.86
Super-Resolution > Reference-based Video Super-Resolution	RealMCVSR	RefVSR-ℓ1	https://arxiv.org/abs/2203.14537v1	PSNR	34.74
Super-Resolution > Reference-based Video Super-Resolution	RealMCVSR	RefVSR-small-ℓ1	https://arxiv.org/abs/2203.14537v1	PSNR	33.88
Super-Resolution > Reference-based Video Super-Resolution	RealMCVSR	IconVSR-ℓch [chan2021basicvsr]	https://arxiv.org/abs/2203.14537v1	PSNR	33.80
Super-Resolution > Reference-based Video Super-Resolution	RealMCVSR	BasicVSR-ℓch [chan2021basicvsr]	https://arxiv.org/abs/2203.14537v1	PSNR	33.66
Super-Resolution > Reference-based Video Super-Resolution	RealMCVSR	EDVR-ℓch [wang2019edvr]	https://arxiv.org/abs/2203.14537v1	PSNR	33.47
Super-Resolution > Reference-based Video Super-Resolution	RealMCVSR	EDVR-M-ℓch [wang2019edvr]	https://arxiv.org/abs/2203.14537v1	PSNR	33.26
Super-Resolution > Reference-based Video Super-Resolution	RealMCVSR	DCSR-ℓ1 [wang2021DCSR]	https://arxiv.org/abs/2203.14537v1	PSNR	32.43
Super-Resolution > Reference-based Video Super-Resolution	RealMCVSR	RCAN-ℓ1 [zhang2018rcan]	https://arxiv.org/abs/2203.14537v1	PSNR	31.07
Super-Resolution > Reference-based Video Super-Resolution	RealMCVSR	TTSR-ℓ1 [yang2020TTSR]	https://arxiv.org/abs/2203.14537v1	PSNR	30.83
Electroencephalogram (EEG)	HS-SSVEP	MultitaskSSVEP	https://ieeexplore.ieee.org/abstract/document/9283310	Accuracy (5-fold)	92.2
Electroencephalogram (EEG)	SEED	DBN	https://doi.org/10.1109/TAMD.2015.2431497	Accuracy	86.08
Electroencephalogram (EEG)	SEED-IV	BiHDM	http://arxiv.org/abs/1906.01704v1	Accuracy	74.35
Electroencephalogram (EEG)	SEED-IV	DGCNN	https://doi.org/10.1109/taffc.2018.2817622	Accuracy	69.88
Electroencephalogram (EEG)	SEED-IV	DBN	https://doi.org/10.1109/TAMD.2015.2431497	Accuracy	66.77
Electroencephalogram (EEG) > Eeg Decoding	CWL EEG/fMRI Dataset	BEIRA	https://arxiv.org/abs/2211.02024v2	Pearson Correlation	0.44
Electroencephalogram (EEG) > Eeg Decoding > EEG Signal Classification	.	Bipolar Neural Network	https://www.researchgate.net/publication/309967859_Imagined_Speech_Classification_using_EEG	Accuracy (% )	44
Electroencephalogram (EEG) > Attention Score Prediction	PhyAAt	SVM	https://arxiv.org/abs/2005.11577v1	MAE	29.65
Electroencephalogram (EEG) > Noise Level Prediction	PhyAAt	SVM	https://arxiv.org/abs/2005.11577v1	MAE	4.75
Electroencephalogram (EEG) > Semanticity prediction	PhyAAt	SVM	https://arxiv.org/abs/2005.11577v1	Accuracy	56
Electroencephalogram (EEG) > Semanticity prediction	VizNet	DCoM-Single-DistilBERT	https://arxiv.org/abs/2106.12871v1	F1 score	0.925
Electroencephalogram (EEG) > LWR Classification	PhyAAt	SVM	https://arxiv.org/abs/2005.11577v1	Accuracy	81
X-Ray > Low-Dose X-Ray Ct Reconstruction	X3D	SAX-NeRF	https://arxiv.org/abs/2311.10959v3	PSNR	37.25
X-Ray > Low-Dose X-Ray Ct Reconstruction	X3D	SAX-NeRF	https://arxiv.org/abs/2311.10959v3	SSIM	0.9753
X-Ray > Low-Dose X-Ray Ct Reconstruction	X3D	NAF	https://arxiv.org/abs/2209.14540v1	PSNR	34.76
X-Ray > Low-Dose X-Ray Ct Reconstruction	X3D	NAF	https://arxiv.org/abs/2209.14540v1	SSIM	0.9535
X-Ray > Low-Dose X-Ray Ct Reconstruction	X3D	TensoRF	https://arxiv.org/abs/2203.09517v2	PSNR	33.78
X-Ray > Low-Dose X-Ray Ct Reconstruction	X3D	TensoRF	https://arxiv.org/abs/2203.09517v2	SSIM	0.9387
X-Ray > Low-Dose X-Ray Ct Reconstruction	X3D	NeAT	https://arxiv.org/abs/2202.02171v1	PSNR	33.41
X-Ray > Low-Dose X-Ray Ct Reconstruction	X3D	NeAT	https://arxiv.org/abs/2202.02171v1	SSIM	0.9447
X-Ray > Low-Dose X-Ray Ct Reconstruction	X3D	SART	https://arxiv.org/abs/2311.10959v3	PSNR	32.33
X-Ray > Low-Dose X-Ray Ct Reconstruction	X3D	SART	https://arxiv.org/abs/2311.10959v3	SSIM	0.9342
X-Ray > Low-Dose X-Ray Ct Reconstruction	X3D	ASD-POCS	https://arxiv.org/abs/2311.10959v3	PSNR	32.32
X-Ray > Low-Dose X-Ray Ct Reconstruction	X3D	ASD-POCS	https://arxiv.org/abs/2311.10959v3	SSIM	0.9400
X-Ray > Low-Dose X-Ray Ct Reconstruction	X3D	NeRF	https://arxiv.org/abs/2003.08934v2	PSNR	32.15
X-Ray > Low-Dose X-Ray Ct Reconstruction	X3D	NeRF	https://arxiv.org/abs/2003.08934v2	SSIM	0.9354
X-Ray > Low-Dose X-Ray Ct Reconstruction	X3D	InTomo	http://openaccess.thecvf.com//content/ICCV2021/html/Zang_IntraTomo_Self-Supervised_Learning-Based_Tomography_via_Sinogram_Synthesis_and_Prediction_ICCV_2021_paper.html	PSNR	30.29
X-Ray > Low-Dose X-Ray Ct Reconstruction	X3D	InTomo	http://openaccess.thecvf.com//content/ICCV2021/html/Zang_IntraTomo_Self-Supervised_Learning-Based_Tomography_via_Sinogram_Synthesis_and_Prediction_ICCV_2021_paper.html	SSIM	0.9189
X-Ray > Low-Dose X-Ray Ct Reconstruction	X3D	FDK	https://arxiv.org/abs/2311.10959v3	PSNR	25.12
X-Ray > Low-Dose X-Ray Ct Reconstruction	X3D	FDK	https://arxiv.org/abs/2311.10959v3	SSIM	0.6422
Cancer > Breast Cancer Detection	Breast Cancer Coimbra Data Set	CFS-TSK+	https://arxiv.org/abs/2101.03581v3	Mean Accuracy	79.17
Cancer > Breast Cancer Detection	Breast cancer Wisconsin_class 4	XBNET	https://arxiv.org/abs/2106.05239v3	Accuracy	96.49
Cancer > Breast Cancer Detection	Breast cancer Wisconsin_class 4	XBNET	https://arxiv.org/abs/2106.05239v3	Average Precision	0.95
Cancer > Breast Cancer Detection	CMMD	Luminal vs Non Luminal	https://arxiv.org/abs/2301.09282v1	AUC	0.6688
Cancer > Breast Cancer Detection	BreakHis	IRv2-CXL	https://arxiv.org/abs/2209.01380v1	1:1 Accuracy	96.46
Cancer > Breast Cancer Detection	BreakHis	Breast-NET	https://link.springer.com/article/10.1007/s00521-024-10298-9	1:1 Accuracy	98.11
Cancer > Lung Cancer Diagnosis	National Lung Screening Trial (NLST)	ResNet50 SWS++	https://arxiv.org/abs/2405.04605v2	AUC	0.81 (0.79-0.82)
Cancer > Lung Cancer Diagnosis	Duke Lung Nodule Dataset 2024	ResNet50 SWS++	https://arxiv.org/abs/2405.04605v2	AUC	0.71 ± 0.10
Cancer > Skin Cancer Classification	ISIC 2017	VGG19	https://arxiv.org/abs/2212.05116v3	Accuracy Improvement	17%
Cancer > Breast Cancer Histology Image Classification	ICIAR 2018 Grand Challenge on Breast Cancer Histology Images	ResNet-152	https://www.researchgate.net/publication/374471906_Breast_cancer_histology_classification_using_Deep_Residual_Networks	Accuracy (% )	83

Super-Resolution > Image Rescaling

DIV2K val-q30-4x

T-SAIN

https://arxiv.org/abs/2412.13508v1

PSNR

28.08

Super-Resolution > Image Rescaling

DIV2K val-q30-4x

T-SAIN

https://arxiv.org/abs/2412.13508v1

SSIM

0.7893

Super-Resolution > Image Rescaling

DIV2K val-q30-4x

SAIN

https://arxiv.org/abs/2303.02353v2

PSNR

27.90

Super-Resolution > Image Rescaling

DIV2K val-q30-4x

SAIN

https://arxiv.org/abs/2303.02353v2

SSIM

0.7745

Super-Resolution > Image Rescaling

DIV2K val-q30-4x

IRN

https://arxiv.org/abs/2005.05650v1

PSNR

25.98

Super-Resolution > Image Rescaling

DIV2K val-q30-4x

IRN

https://arxiv.org/abs/2005.05650v1

SSIM

0.6867

Super-Resolution > Image Rescaling

DIV2K val-q30-4x

HCFlow

https://arxiv.org/abs/2108.05301v1

PSNR

25.89

Super-Resolution > Image Rescaling

DIV2K val-q30-4x

HCFlow

https://arxiv.org/abs/2108.05301v1

SSIM

0.6838

Super-Resolution > Image Rescaling

BSD100-4x

T-IRN

https://arxiv.org/abs/2412.13508v1

PSNR

31.64

Super-Resolution > Image Rescaling

BSD100-4x

T-IRN

https://arxiv.org/abs/2412.13508v1

SSIM

0.8837

Super-Resolution > Image Rescaling

BSD100-4x

IRN

https://arxiv.org/abs/2005.05650v1

PSNR

31.64

Super-Resolution > Image Rescaling

BSD100-4x

IRN

https://arxiv.org/abs/2005.05650v1

SSIM

0.8826

Super-Resolution > Image Rescaling

DIV2K val-4x

T-IRN

https://arxiv.org/abs/2412.13508v1

PSNR

35.10

Super-Resolution > Image Rescaling

DIV2K val-4x

T-IRN

https://arxiv.org/abs/2412.13508v1

SSIM

0.9328

Super-Resolution > Image Rescaling

DIV2K val-4x

IRN

https://arxiv.org/abs/2005.05650v1

PSNR

35.07

Super-Resolution > Image Rescaling

DIV2K val-4x

IRN

https://arxiv.org/abs/2005.05650v1

SSIM

0.9318

Super-Resolution > Image Rescaling

Set14-4x

T-IRN

https://arxiv.org/abs/2412.13508v1

PSNR

32.70

Super-Resolution > Image Rescaling

Set14-4x

T-IRN

https://arxiv.org/abs/2412.13508v1

SSIM

0.9003

Super-Resolution > Image Rescaling

Set14-4x

IRN

https://arxiv.org/abs/2005.05650v1

PSNR

32.67

Super-Resolution > Image Rescaling

Set14-4x

IRN

https://arxiv.org/abs/2005.05650v1

SSIM

0.9015

Super-Resolution > Image Rescaling

DIV2K val-q90-4x

T-SAIN

https://arxiv.org/abs/2412.13508v1

PSNR

30.92

Super-Resolution > Image Rescaling

DIV2K val-q90-4x

T-SAIN

https://arxiv.org/abs/2412.13508v1

SSIM

0.8517

Super-Resolution > Image Rescaling

DIV2K val-q90-4x

SAIN

https://arxiv.org/abs/2303.02353v2

PSNR

30.31

Super-Resolution > Image Rescaling

DIV2K val-q90-4x

SAIN

https://arxiv.org/abs/2303.02353v2

SSIM

0.8367

Super-Resolution > Image Rescaling

DIV2K val-q90-4x

IRN

https://arxiv.org/abs/2005.05650v1

PSNR

28.42

Super-Resolution > Image Rescaling

DIV2K val-q90-4x

IRN

https://arxiv.org/abs/2005.05650v1

SSIM

0.7777

Super-Resolution > Image Rescaling

DIV2K val-q90-4x

HCFlow

https://arxiv.org/abs/2108.05301v1

PSNR

27.41

Super-Resolution > Image Rescaling

DIV2K val-q90-4x

HCFlow

https://arxiv.org/abs/2108.05301v1

SSIM

0.7485

Super-Resolution > Image Rescaling

Urban100-4x

IRN

https://arxiv.org/abs/2005.05650v1

PSNR

31.41

Super-Resolution > Image Rescaling

Urban100-4x

IRN

https://arxiv.org/abs/2005.05650v1

SSIM

0.9157

Super-Resolution > Image Rescaling

Urban100-4x

T-IRN

https://arxiv.org/abs/2412.13508v1

PSNR

31.19

Super-Resolution > Image Rescaling

Urban100-4x

T-IRN

https://arxiv.org/abs/2412.13508v1

SSIM

0.9132

Super-Resolution > Image Rescaling

Set14-2x

T-IRN

https://arxiv.org/abs/2412.13508v1

PSNR

41.70

Super-Resolution > Image Rescaling

Set14-2x

T-IRN

https://arxiv.org/abs/2412.13508v1

SSIM

0.9809

Super-Resolution > Image Rescaling

Set14-2x

IRN

https://arxiv.org/abs/2005.05650v1

PSNR

40.79

Super-Resolution > Image Rescaling

Set14-2x

IRN

https://arxiv.org/abs/2005.05650v1

SSIM

0.9778

Super-Resolution > Image Rescaling

DIV2K val-q50-2x

T-SAIN

https://arxiv.org/abs/2412.13508v1

PSNR

33.71

Super-Resolution > Image Rescaling

DIV2K val-q50-2x

T-SAIN

https://arxiv.org/abs/2412.13508v1

SSIM

0.9210

Super-Resolution > Image Rescaling

DIV2K val-q50-2x

SAIN

https://arxiv.org/abs/2303.02353v2

PSNR

33.17

Super-Resolution > Image Rescaling

DIV2K val-q50-2x

SAIN

https://arxiv.org/abs/2303.02353v2

SSIM

0.9082

Super-Resolution > Image Rescaling

DIV2K val-q50-2x

IRN

https://arxiv.org/abs/2005.05650v1

PSNR

30.20

Super-Resolution > Image Rescaling

DIV2K val-q50-2x

IRN

https://arxiv.org/abs/2005.05650v1

SSIM

0.8342

Super-Resolution > Image Rescaling

DIV2K val-q50-4x

T-SAIN

https://arxiv.org/abs/2412.13508v1

PSNR

29.43

Super-Resolution > Image Rescaling

DIV2K val-q50-4x

T-SAIN

https://arxiv.org/abs/2412.13508v1

SSIM

0.8237

Super-Resolution > Image Rescaling

DIV2K val-q50-4x

SAIN

https://arxiv.org/abs/2303.02353v2

PSNR

29.05

Super-Resolution > Image Rescaling

DIV2K val-q50-4x

SAIN

https://arxiv.org/abs/2303.02353v2

SSIM

0.8088

Super-Resolution > Image Rescaling

DIV2K val-q50-4x

IRN

https://arxiv.org/abs/2005.05650v1

PSNR

26.62

Super-Resolution > Image Rescaling

DIV2K val-q50-4x

IRN

https://arxiv.org/abs/2005.05650v1

SSIM

0.7096

Super-Resolution > Image Rescaling

DIV2K val-q50-4x

HCFlow

https://arxiv.org/abs/2108.05301v1

PSNR

26.38

Super-Resolution > Image Rescaling

DIV2K val-q50-4x

HCFlow

https://arxiv.org/abs/2108.05301v1

SSIM

0.7029

Super-Resolution > Reference-based Video Super-Resolution

RealMCVSR

RefVSR-IR-ℓ1

https://arxiv.org/abs/2203.14537v1

PSNR

34.86

Super-Resolution > Reference-based Video Super-Resolution

RealMCVSR

RefVSR-ℓ1

https://arxiv.org/abs/2203.14537v1

PSNR

34.74

Super-Resolution > Reference-based Video Super-Resolution

RealMCVSR

RefVSR-small-ℓ1

https://arxiv.org/abs/2203.14537v1

PSNR

33.88

Super-Resolution > Reference-based Video Super-Resolution

RealMCVSR

IconVSR-ℓch [chan2021basicvsr]

https://arxiv.org/abs/2203.14537v1

PSNR

33.80

Super-Resolution > Reference-based Video Super-Resolution

RealMCVSR

BasicVSR-ℓch [chan2021basicvsr]

https://arxiv.org/abs/2203.14537v1

PSNR

33.66

Super-Resolution > Reference-based Video Super-Resolution

RealMCVSR

EDVR-ℓch [wang2019edvr]

https://arxiv.org/abs/2203.14537v1

PSNR

33.47

Super-Resolution > Reference-based Video Super-Resolution

RealMCVSR

EDVR-M-ℓch [wang2019edvr]

https://arxiv.org/abs/2203.14537v1

PSNR

33.26

Super-Resolution > Reference-based Video Super-Resolution

RealMCVSR

DCSR-ℓ1 [wang2021DCSR]

https://arxiv.org/abs/2203.14537v1

PSNR

32.43

Super-Resolution > Reference-based Video Super-Resolution

RealMCVSR

RCAN-ℓ1 [zhang2018rcan]

https://arxiv.org/abs/2203.14537v1

PSNR

31.07

Super-Resolution > Reference-based Video Super-Resolution

RealMCVSR

TTSR-ℓ1 [yang2020TTSR]

https://arxiv.org/abs/2203.14537v1

PSNR

30.83

Electroencephalogram (EEG)

HS-SSVEP

MultitaskSSVEP

https://ieeexplore.ieee.org/abstract/document/9283310

Accuracy (5-fold)

92.2

Electroencephalogram (EEG)

　SEED

DBN

https://doi.org/10.1109/TAMD.2015.2431497

Accuracy

86.08

Electroencephalogram (EEG)

SEED-IV

BiHDM

http://arxiv.org/abs/1906.01704v1

Accuracy

74.35

Electroencephalogram (EEG)

SEED-IV

DGCNN

https://doi.org/10.1109/taffc.2018.2817622

Accuracy

69.88

Electroencephalogram (EEG)

SEED-IV

DBN

https://doi.org/10.1109/TAMD.2015.2431497

Accuracy

66.77

Electroencephalogram (EEG) > Eeg Decoding

CWL EEG/fMRI Dataset

BEIRA

https://arxiv.org/abs/2211.02024v2

Pearson Correlation

0.44

Electroencephalogram (EEG) > Eeg Decoding > EEG Signal Classification

.

Bipolar Neural Network

https://www.researchgate.net/publication/309967859_Imagined_Speech_Classification_using_EEG

Accuracy (% )

44

Electroencephalogram (EEG) > Attention Score Prediction

PhyAAt

SVM

https://arxiv.org/abs/2005.11577v1

MAE

29.65

Electroencephalogram (EEG) > Noise Level Prediction

PhyAAt

SVM

https://arxiv.org/abs/2005.11577v1

MAE

4.75

Electroencephalogram (EEG) > Semanticity prediction

PhyAAt

SVM

https://arxiv.org/abs/2005.11577v1

Accuracy

56

Electroencephalogram (EEG) > Semanticity prediction

VizNet

DCoM-Single-DistilBERT

https://arxiv.org/abs/2106.12871v1

F1 score

0.925

Electroencephalogram (EEG) > LWR Classification

PhyAAt

SVM

https://arxiv.org/abs/2005.11577v1

Accuracy

81

X-Ray > Low-Dose X-Ray Ct Reconstruction

X3D

SAX-NeRF

https://arxiv.org/abs/2311.10959v3

PSNR

37.25

X-Ray > Low-Dose X-Ray Ct Reconstruction

X3D

SAX-NeRF

https://arxiv.org/abs/2311.10959v3

SSIM

0.9753

X-Ray > Low-Dose X-Ray Ct Reconstruction

X3D

NAF

https://arxiv.org/abs/2209.14540v1

PSNR

34.76

X-Ray > Low-Dose X-Ray Ct Reconstruction

X3D

NAF

https://arxiv.org/abs/2209.14540v1

SSIM

0.9535

X-Ray > Low-Dose X-Ray Ct Reconstruction

X3D

TensoRF

https://arxiv.org/abs/2203.09517v2

PSNR

33.78

X-Ray > Low-Dose X-Ray Ct Reconstruction

X3D

TensoRF

https://arxiv.org/abs/2203.09517v2

SSIM

0.9387

X-Ray > Low-Dose X-Ray Ct Reconstruction

X3D

NeAT

https://arxiv.org/abs/2202.02171v1

PSNR

33.41

X-Ray > Low-Dose X-Ray Ct Reconstruction

X3D

NeAT

https://arxiv.org/abs/2202.02171v1

SSIM

0.9447

X-Ray > Low-Dose X-Ray Ct Reconstruction

X3D

SART

https://arxiv.org/abs/2311.10959v3

PSNR

32.33

X-Ray > Low-Dose X-Ray Ct Reconstruction

X3D

SART

https://arxiv.org/abs/2311.10959v3

SSIM

0.9342

X-Ray > Low-Dose X-Ray Ct Reconstruction

X3D

ASD-POCS

https://arxiv.org/abs/2311.10959v3

PSNR

32.32

X-Ray > Low-Dose X-Ray Ct Reconstruction

X3D

ASD-POCS

https://arxiv.org/abs/2311.10959v3

SSIM

0.9400

X-Ray > Low-Dose X-Ray Ct Reconstruction

X3D

NeRF

https://arxiv.org/abs/2003.08934v2

PSNR

32.15

X-Ray > Low-Dose X-Ray Ct Reconstruction

X3D

NeRF

https://arxiv.org/abs/2003.08934v2

SSIM

0.9354

X-Ray > Low-Dose X-Ray Ct Reconstruction

X3D

InTomo

http://openaccess.thecvf.com//content/ICCV2021/html/Zang_IntraTomo_Self-Supervised_Learning-Based_Tomography_via_Sinogram_Synthesis_and_Prediction_ICCV_2021_paper.html

PSNR

30.29

X-Ray > Low-Dose X-Ray Ct Reconstruction

X3D

InTomo

http://openaccess.thecvf.com//content/ICCV2021/html/Zang_IntraTomo_Self-Supervised_Learning-Based_Tomography_via_Sinogram_Synthesis_and_Prediction_ICCV_2021_paper.html

SSIM

0.9189

X-Ray > Low-Dose X-Ray Ct Reconstruction

X3D

FDK

https://arxiv.org/abs/2311.10959v3

PSNR

25.12

X-Ray > Low-Dose X-Ray Ct Reconstruction

X3D

FDK

https://arxiv.org/abs/2311.10959v3

SSIM

0.6422

Cancer > Breast Cancer Detection

Breast Cancer Coimbra Data Set

CFS-TSK+

https://arxiv.org/abs/2101.03581v3

Mean Accuracy

79.17

Cancer > Breast Cancer Detection

Breast cancer Wisconsin_class 4

XBNET

https://arxiv.org/abs/2106.05239v3

Accuracy

96.49

Cancer > Breast Cancer Detection

Breast cancer Wisconsin_class 4

XBNET

https://arxiv.org/abs/2106.05239v3

Average Precision

0.95

Cancer > Breast Cancer Detection

CMMD

Luminal vs Non Luminal

https://arxiv.org/abs/2301.09282v1

AUC

0.6688

Cancer > Breast Cancer Detection

BreakHis

IRv2-CXL

https://arxiv.org/abs/2209.01380v1

1:1 Accuracy

96.46

Cancer > Breast Cancer Detection

BreakHis

Breast-NET

https://link.springer.com/article/10.1007/s00521-024-10298-9

1:1 Accuracy

98.11

Cancer > Lung Cancer Diagnosis

National Lung Screening Trial (NLST)

ResNet50 SWS++

https://arxiv.org/abs/2405.04605v2

AUC

0.81 (0.79-0.82)

Cancer > Lung Cancer Diagnosis

Duke Lung Nodule Dataset 2024

ResNet50 SWS++

https://arxiv.org/abs/2405.04605v2

AUC

0.71 ± 0.10

Cancer > Skin Cancer Classification

ISIC 2017

VGG19

https://arxiv.org/abs/2212.05116v3

Accuracy Improvement

17%

Cancer > Breast Cancer Histology Image Classification

ICIAR 2018 Grand Challenge on Breast Cancer Histology Images

ResNet-152

https://www.researchgate.net/publication/374471906_Breast_cancer_histology_classification_using_Deep_Residual_Networks

Accuracy (% )

83