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 8251–8275 of 10307 papers

TitleStatusHype
TransTailor: Pruning the Pre-trained Model for Improved Transfer Learning—0
Triangular Transfer: Freezing the Pivot for Triangular Machine Translation—0
TripHLApan: predicting HLA molecules binding peptides based on triple coding matrix and transfer learning—0
Trivial Transfer Learning for Low-Resource Neural Machine Translation—0
TrNews: Heterogeneous User-Interest Transfer Learning for News Recommendation—0
Trustworthy Transfer Learning: A Survey—0
TTUI at SemEval-2020 Task 11: Propaganda Detection with Transfer Learning and Ensembles—0
TuneNSearch: a hybrid transfer learning and local search approach for solving vehicle routing problems—0
Tuning a SAM-Based Model with Multi-Cognitive Visual Adapter to Remote Sensing Instance Segmentation—0
Turbo2K: Towards Ultra-Efficient and High-Quality 2K Video Synthesis—0
TweetDrought: A Deep-Learning Drought Impacts Recognizer based on Twitter Data—0
Two Front-Ends, One Model : Fusing Heterogeneous Speech Features for Low Resource ASR with Multilingual Pre-Training—0
Two-stage architectural fine-tuning with neural architecture search using early-stopping in image classification—0
Two-Staged Acoustic Modeling Adaption for Robust Speech Recognition by the Example of German Oral History Interviews—0
Two Wrongs Can Make a Right: A Transfer Learning Approach for Chemical Discovery with Chemical Accuracy—0
UBC-DLNLP at SemEval-2023 Task 12: Impact of Transfer Learning on African Sentiment Analysis—0
UCAM Biomedical translation at WMT19: Transfer learning multi-domain ensembles—0
UCSD-Adobe at MEDIQA 2021: Transfer Learning and Answer Sentence Selection for Medical Summarization—0
UET at WNUT-2020 Task 2: A Study of Combining Transfer Learning Methods for Text Classification with RoBERTa—0
UHH-LT at SemEval-2019 Task 6: Supervised vs. Unsupervised Transfer Learning for Offensive Language Detection—0
Ukrainian Texts Classification: Exploration of Cross-lingual Knowledge Transfer Approaches—0
Ultrafast-and-Ultralight ConvNet-Based Intelligent Monitoring System for Diagnosing Early-Stage Mpox Anytime and Anywhere—0
Ear-Keeper: Real-time Diagnosis of Ear Lesions Utilizing Ultralight-Ultrafast ConvNet and Large-scale Ear Endoscopic Dataset—0
Ultrasound Image Representation Learning by Modeling Sonographer Visual Attention—0
Unbabel's Participation in the WMT19 Translation Quality Estimation Shared Task—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