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 5051–5075 of 10307 papers

TitleStatusHype
Knowledge Transfer in Deep Reinforcement Learning for Slice-Aware Mobility Robustness Optimization—0
Knowledge Transfer in Model-Based Reinforcement Learning Agents for Efficient Multi-Task Learning—0
Knowledge Transfer Pre-training—0
Knowledge Transfer via Pre-training for Recommendation: A Review and Prospect—0
Knowledge Transfer via Student-Teacher Collaboration—0
Knowledge Transfer with Jacobian Matching—0
Knowledge Transfer with Medical Language Embeddings—0
Knowledge Transfer with Visual Prompt in multi-modal Dialogue Understanding and Generation—0
Know Where You're Going: Meta-Learning for Parameter-Efficient Fine-Tuning—0
Koopman-based Deep Learning for Nonlinear System Estimation—0
kpfriends at SemEval-2022 Task 2: NEAMER -- Named Entity Augmented Multi-word Expression Recognizer—0
kpfriends at SemEval-2022 Task 2: NEAMER - Named Entity Augmented Multi-word Expression Recognizer—0
Krylov Subspace Recycling for Fast Iterative Least-Squares in Machine Learning—0
KTAN: Knowledge Transfer Adversarial Network—0
KTCR: Improving Implicit Hate Detection with Knowledge Transfer driven Concept Refinement—0
KU\_ai at MEDIQA 2019: Domain-specific Pre-training and Transfer Learning for Medical NLI—0
KubeEdge-Sedna v0.3: Towards Next-Generation Automatically Customized AI Engineering Scheme—0
L3 Ensembles: Lifelong Learning Approach for Ensemble of Foundational Language Models—0
Label Alignment and Reassignment with Generalist Large Language Model for Enhanced Cross-Domain Named Entity Recognition—0
Label-aware Double Transfer Learning for Cross-Specialty Medical Named Entity Recognition—0
Labeled Data Selection for Category Discovery—0
Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directions—0
Label Efficient Learning of Transferable Representations acrosss Domains and Tasks—0
Label Efficient Learning of Transferable Representations across Domains and Tasks—0
Label-efficient Time Series Representation Learning: A Review—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