SOTAVerified

Transfer Learning

Transfer Learning is a machine learning technique where a model trained on one task is re-purposed and fine-tuned for a related, but different task. The idea behind transfer learning is to leverage the knowledge learned from a pre-trained model to solve a new, but related problem. This can be useful in situations where there is limited data available to train a new model from scratch, or when the new task is similar enough to the original task that the pre-trained model can be adapted to the new problem with only minor modifications.

( Image credit: Subodh Malgonde )

Papers

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Showing 8676–8700 of 10307 papers

TitleStatusHype
Knee menisci segmentation and relaxometry of 3D ultrashort echo time (UTE) cones MR imaging using attention U-Net with transfer learning—0
Distilling Knowledge From a Deep Pose Regressor Network—0
Cross-lingual Transfer Learning and Multitask Learning for Capturing Multiword Expressions—0
KU\_ai at MEDIQA 2019: Domain-specific Pre-training and Transfer Learning for Medical NLI—0
PANLP at MEDIQA 2019: Pre-trained Language Models, Transfer Learning and Knowledge Distillation—0
MIDAS@SMM4H-2019: Identifying Adverse Drug Reactions and Personal Health Experience Mentions from Twitter—0
CUNI Submission for Low-Resource Languages in WMT News 2019—0
hULMonA: The Universal Language Model in ArabicCode0
Robust Deep Sensing Through Transfer Learning in Cognitive Radio—0
The RWTH Aachen University Machine Translation Systems for WMT 2019—0
Improving classification of Adverse Drug Reactions through Using Sentiment Analysis and Transfer Learning—0
Improved Generalization of Arabic Text Classifiers—0
Biologically inspired sleep algorithm for artificial neural networks—0
Detecting and Extracting of Adverse Drug Reaction Mentioning Tweets with Multi-Head Self Attention—0
A Survey on Deep Learning of Small Sample in Biomedical Image AnalysisCode0
Exploring Transfer Learning and Domain Data Selection for the Biomedical Translation—0
Cross-domain Network Representations—0
MSIT\_SRIB at MEDIQA 2019: Knowledge Directed Multi-task Framework for Natural Language Inference in Clinical Domain.—0
NICT's Supervised Neural Machine Translation Systems for the WMT19 Translation Robustness Task—0
Huawei's NMT Systems for the WMT 2019 Biomedical Translation Task—0
NICT's Supervised Neural Machine Translation Systems for the WMT19 News Translation Task—0
Transfer Learning from Pre-trained BERT for Pronoun Resolution—0
The University of Maryland's Kazakh-English Neural Machine Translation System at WMT19—0
The Universitat d'Alacant Submissions to the English-to-Kazakh News Translation Task at WMT 2019—0
Transferring Knowledge from Discourse to Arguments: A Case Study with Scientific Abstracts—0
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All datasetsOffice-Home100 sleep nights of 8 caregiversBanglaLekha Isolated DatasetCOCO70KITTI Object Tracking Evaluation 2012Retinal Fundus MultiDisease Image Dataset (RFMiD)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1APCLIPAccuracy84.2—Unverified
2DFA-ENTAccuracy69.2—Unverified
3DFA-SAFNAccuracy69.1—Unverified
4EasyTLAccuracy63.3—Unverified
5MEDAAccuracy60.3—Unverified
#ModelMetricClaimedVerifiedStatus
1CNN10-20% Mask PSNR3.23—Unverified
#ModelMetricClaimedVerifiedStatus
1Chatterjee, Dutta et al.[1]Accuracy96.12—Unverified
#ModelMetricClaimedVerifiedStatus
1Co-TuningAccuracy85.65—Unverified
#ModelMetricClaimedVerifiedStatus
1Physical AccessEER5.74—Unverified
#ModelMetricClaimedVerifiedStatus
1riadd.aucmediAUROC0.95—Unverified