Tasks SotA Datasets Papers Methods Submit About

Papers With Code 2

A community resource for machine learning research: papers, code, benchmarks, and state-of-the-art results.

Explore

Notable Benchmarks All SotA Datasets Papers Methods

Community

Submit Results About

Data sourced from the PWC Archive (CC-BY-SA 4.0). Built by the community, for the community.

Models/FCN

FCN

Reported on 33 benchmarks across 11 tasks · 6 papers · 23 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Miscellaneous9 results

Click-Through Rate PredictiononKKBox
AUC· 2024-07-18
0.8557
SOTA
FCN: Fusing Exponential and Linear Cross Network for Click-Through Rate Prediction arXiv:2407.13349
Click-Through Rate PredictiononiPinYou
LogLoss· 2024-07-18
0.005535
SOTA
FCN: Fusing Exponential and Linear Cross Network for Click-Through Rate Prediction arXiv:2407.13349
Click-Through Rate PredictiononCriteo
AUC· 2024-07-18
0.8162
best: 0.8163 (QNN-α)
SOTA
FCN: Fusing Exponential and Linear Cross Network for Click-Through Rate Prediction arXiv:2407.13349
Click-Through Rate PredictiononCriteo
Log Loss· 2024-07-18
0.4358
SOTA
FCN: Fusing Exponential and Linear Cross Network for Click-Through Rate Prediction arXiv:2407.13349
Click-Through Rate PredictiononKDD12
AUC· 2024-07-18
0.8098
SOTA
FCN: Fusing Exponential and Linear Cross Network for Click-Through Rate Prediction arXiv:2407.13349
Click-Through Rate PredictiononKDD12
Log Loss· 2024-07-18
0.1494
SOTA
FCN: Fusing Exponential and Linear Cross Network for Click-Through Rate Prediction arXiv:2407.13349
Click-Through Rate PredictiononAvazu
AUC· 2024-07-18
0.797
best: 0.8062 (OptInter)
FCN: Fusing Exponential and Linear Cross Network for Click-Through Rate Prediction arXiv:2407.13349
Click-Through Rate PredictiononAvazu
LogLoss· 2024-07-18
0.3695
best: 0.3637 (OptInter)
FCN: Fusing Exponential and Linear Cross Network for Click-Through Rate Prediction arXiv:2407.13349
Click-Through Rate PredictiononiPinYou
AUC· 2024-07-18
0.7856
best: 0.8174 (OPNN)
FCN: Fusing Exponential and Linear Cross Network for Click-Through Rate Prediction arXiv:2407.13349

Medical8 results

Semantic SegmentationonFine-Grained Grass Segmentation Dataset
mIoU· 2014-11-14
47.47
best: 51.96 (D2LS)
SOTA
Fully Convolutional Networks for Semantic Segmentation arXiv:1411.4038
Semantic SegmentationonSELMA
mIoU· 2014-11-14
68.2
best: 91.7 (CMX)
SOTA
Fully Convolutional Networks for Semantic Segmentation arXiv:1411.4038
Semantic SegmentationonEvent-based Segmentation Dataset
mIoU· 2014-11-14
59.6
best: 87.05 (Bimodal SegNet)
SOTA
Fully Convolutional Networks for Semantic Segmentation arXiv:1411.4038
Semantic SegmentationonTrans10K
GFLOPs· 2014-11-14
42.23
best: 198 (DANet)
SOTA
Fully Convolutional Networks for Semantic Segmentation arXiv:1411.4038
Semantic SegmentationonADE20K
Validation mIoU· uses extra data· 2014-11-14
29.39
best: 63.6 (ViT-P (InternImage-H))
SOTA
Fully Convolutional Networks for Semantic Segmentation arXiv:1411.4038
Semantic SegmentationonCrackVision12K
mIoU· 2014-11-14
0.59842
best: 0.62982 (Hybrid-Segmentor)
SOTA
Fully Convolutional Networks for Semantic Segmentation arXiv:1411.4038
Semantic SegmentationonChesapeakeRSC
DWR· 2024-01-12
10.7
best: 46.5 (U-Net (ResNet-18))
Seeing the roads through the trees: A benchmark for modeling spatial dependencies with aerial imagery arXiv:2401.06762
Semantic SegmentationonSUN-RGBD
Mean IoU· 2016-05-20
27.39
best: 54.6 (GeminiFusion (Swin-Large))
Fully Convolutional Networks for Semantic Segmentation arXiv:1605.06211

Audio8 results

10-shot image generationonFine-Grained Grass Segmentation Dataset
mIoU· 2014-11-14
47.47
best: 51.96 (D2LS)
SOTA
Fully Convolutional Networks for Semantic Segmentation arXiv:1411.4038
10-shot image generationonSELMA
mIoU· 2014-11-14
68.2
best: 91.7 (CMX)
SOTA
Fully Convolutional Networks for Semantic Segmentation arXiv:1411.4038
10-shot image generationonEvent-based Segmentation Dataset
mIoU· 2014-11-14
59.6
best: 87.05 (Bimodal SegNet)
SOTA
Fully Convolutional Networks for Semantic Segmentation arXiv:1411.4038
10-shot image generationonTrans10K
GFLOPs· 2014-11-14
42.23
best: 198 (DANet)
SOTA
Fully Convolutional Networks for Semantic Segmentation arXiv:1411.4038
10-shot image generationonADE20K
Validation mIoU· uses extra data· 2014-11-14
29.39
best: 63.6 (ViT-P (InternImage-H))
SOTA
Fully Convolutional Networks for Semantic Segmentation arXiv:1411.4038
10-shot image generationonCrackVision12K
mIoU· 2014-11-14
0.59842
best: 0.62982 (Hybrid-Segmentor)
SOTA
Fully Convolutional Networks for Semantic Segmentation arXiv:1411.4038
10-shot image generationonChesapeakeRSC
DWR· 2024-01-12
10.7
best: 46.5 (U-Net (ResNet-18))
Seeing the roads through the trees: A benchmark for modeling spatial dependencies with aerial imagery arXiv:2401.06762
10-shot image generationonSUN-RGBD
Mean IoU· 2016-05-20
27.39
best: 54.6 (GeminiFusion (Swin-Large))
Fully Convolutional Networks for Semantic Segmentation arXiv:1605.06211

Computer Vision4 results

Object DetectiononCholec80
mAP· 2018-06-14
87.4
best: 93.5 (MoCo V2 Surg SSL - FCN head)
SOTA
Weakly-Supervised Learning for Tool Localization in Laparoscopic Videos arXiv:1806.05573
Road SegmentationonChesapeakeRSC
DWR· 2024-01-12
10.7
best: 46.5 (U-Net (ResNet-18))
Seeing the roads through the trees: A benchmark for modeling spatial dependencies with aerial imagery arXiv:2401.06762
Font RecognitiononAdobeVFR syn
Top 1 Accuracy· 2021-03-30
78.2
best: 98.97 (DeepFont (S))
FONTNET: On-Device Font Understanding and Prediction Pipeline arXiv:2103.16150
Scene SegmentationonSUN-RGBD
Mean IoU· 2016-05-20
27.39
best: 50.6 (ICM)
Fully Convolutional Networks for Semantic Segmentation arXiv:1605.06211

Methodology4 results

3DonCholec80
mAP· 2018-06-14
87.4
best: 93.5 (MoCo V2 Surg SSL - FCN head)
SOTA
Weakly-Supervised Learning for Tool Localization in Laparoscopic Videos arXiv:1806.05573
2D ClassificationonCholec80
mAP· 2018-06-14
87.4
best: 93.5 (MoCo V2 Surg SSL - FCN head)
SOTA
Weakly-Supervised Learning for Tool Localization in Laparoscopic Videos arXiv:1806.05573
2D Object DetectiononCholec80
mAP· 2018-06-14
87.4
best: 93.5 (MoCo V2 Surg SSL - FCN head)
SOTA
Weakly-Supervised Learning for Tool Localization in Laparoscopic Videos arXiv:1806.05573
16konCholec80
mAP· 2018-06-14
87.4
best: 93.5 (MoCo V2 Surg SSL - FCN head)
SOTA
Weakly-Supervised Learning for Tool Localization in Laparoscopic Videos arXiv:1806.05573