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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 7426–7450 of 10307 papers

TitleStatusHype
NRC Systems for Low Resource German-Upper Sorbian Machine Translation 2020: Transfer Learning with Lexical Modifications—0
Combining Sequence Distillation and Transfer Learning for Efficient Low-Resource Neural Machine Translation Models—0
Huawei’s Submissions to the WMT20 Biomedical Translation Task—0
An Iterative Knowledge Transfer NMT System for WMT20 News Translation Task—0
An Information-Geometric Distance on the Space of TasksCode0
Efficient Transfer Learning for Quality Estimation with Bottleneck Adapter Layer—0
Evaluation of Transfer Learning for Adverse Drug Event (ADE) and Medication Entity Extraction—0
PatchBERT: Just-in-Time, Out-of-Vocabulary Patching—0
SciWING– A Software Toolkit for Scientific Document Processing—0
``I'd rather just go to bed'': Understanding Indirect Answers—0
Hate-Speech and Offensive Language Detection in Roman Urdu—0
TED-CDB: A Large-Scale Chinese Discourse Relation Dataset on TED Talks—0
Cross-lingual sentiment classification in low-resource Bengali languageCode0
Predictive Model Selection for Transfer Learning in Sequence Labeling Tasks—0
A Method for Building a Commonsense Inference Dataset based on Basic Events—0
Learning from Explanations and Demonstrations: A Pilot Study—0
HW-TSC’s Participation at WMT 2020 Quality Estimation Shared Task—0
The LMU Munich System for the WMT20 Very Low Resource Supervised MT Task—0
Active Learning Approaches to Enhancing Neural Machine Translation—0
How Far Can We Go with Data Selection? A Case Study on Semantic Sequence Tagging Tasks—0
Cancer Registry Information Extraction via Transfer Learning—0
BioBERTpt - A Portuguese Neural Language Model for Clinical Named Entity Recognition—0
A multi-source approach for Breton–French hybrid machine translation—0
A Semi-supervised Approach to Generate the Code-Mixed Text using Pre-trained Encoder and Transfer Learning—0
A Joint Multiple Criteria Model in Transfer Learning for Cross-domain Chinese Word Segmentation—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