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Models/PNN

PNN

Reported on 9 benchmarks across 2 tasks · 2 papers · 4 SOTA

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

Miscellaneous6 results

Click-Through Rate PredictiononDianping
AUC· 2016-11-01
0.8445
best: 0.8639 (xDeepFM)
SOTA
Product-based Neural Networks for User Response Prediction arXiv:1611.00144
Click-Through Rate PredictiononMovieLens 20M
AUC· 2016-11-01
0.7321
best: 0.79 (github.com/guotong1988/movielens_dataset)
SOTA
Product-based Neural Networks for User Response Prediction arXiv:1611.00144
Click-Through Rate PredictiononAmazon
AUC· 2016-11-01
0.8679
best: 0.8871 (DIN + Dice Activation)
SOTA
Product-based Neural Networks for User Response Prediction arXiv:1611.00144
Click-Through Rate PredictiononBing News
AUC· 2016-11-01
0.8321
best: 0.84 (xDeepFM)
Product-based Neural Networks for User Response Prediction arXiv:1611.00144
Click-Through Rate PredictiononBing News
Log Loss· 2016-11-01
0.2775
best: 0.2649 (xDeepFM)
Product-based Neural Networks for User Response Prediction arXiv:1611.00144
Click-Through Rate PredictiononDianping
Log Loss· 2016-11-01
0.3424
best: 0.3156 (xDeepFM)
Product-based Neural Networks for User Response Prediction arXiv:1611.00144

Computer Vision3 results

Image FusiononPanCollection
SAM· 2018-06-23
3.6054
SOTA
Pansharpening via Detail Injection Based Convolutional Neural Networks arXiv:1806.08898
Image FusiononPanCollection
ERGAS· 2018-06-23
2.7756
best: 2.7795 (DiCNN)
Pansharpening via Detail Injection Based Convolutional Neural Networks arXiv:1806.08898
Image FusiononPanCollection
Q8· 2018-06-23
0.8797
best: 0.8864 (DiCNN)
Pansharpening via Detail Injection Based Convolutional Neural Networks arXiv:1806.08898