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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 4531–4540 of 10307 papers

Title	Date	Tasks	Status
Knowledge transfer for surgical activity prediction	Nov 15, 2017	Activity PredictionPrediction	—Unverified
Knowledge Transfer from Answer Ranking to Answer Generation	Oct 23, 2022	Answer GenerationQuestion Answering	—Unverified
Knowledge Transfer from High-Resource to Low-Resource Programming Languages for Code LLMs	Aug 19, 2023	Transfer Learning	—Unverified
Knowledge Transfer from Large-scale Pretrained Language Models to End-to-end Speech Recognizers	Feb 16, 2022	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Knowledge Transfer from Teachers to Learners in Growing-Batch Reinforcement Learning	May 5, 2023	Decision Makingreinforcement-learning	—Unverified
Knowledge transfer in deep block-modular neural networks	Jul 24, 2019	Transfer Learning	—Unverified
Knowledge Transfer in Deep Reinforcement Learning for Slice-Aware Mobility Robustness Optimization	Mar 7, 2022	Deep Reinforcement Learningreinforcement-learning	—Unverified
Knowledge Transfer in Model-Based Reinforcement Learning Agents for Efficient Multi-Task Learning	Jan 9, 2025	Model-based Reinforcement LearningMulti-Task Learning	—Unverified
Knowledge Transfer Pre-training	Jun 7, 2015	speech-recognitionSpeech Recognition	—Unverified
Knowledge Transfer via Pre-training for Recommendation: A Review and Prospect	Sep 19, 2020	Recommendation SystemsTransfer Learning	—Unverified

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All datasets Office-Home 100 sleep nights of 8 caregivers BanglaLekha Isolated Dataset COCO70 KITTI Object Tracking Evaluation 2012 Retinal Fundus MultiDisease Image Dataset (RFMiD)

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	APCLIP	Accuracy	84.2	—	Unverified
2	DFA-ENT	Accuracy	69.2	—	Unverified
3	DFA-SAFN	Accuracy	69.1	—	Unverified
4	EasyTL	Accuracy	63.3	—	Unverified
5	MEDA	Accuracy	60.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CNN	10-20% Mask PSNR	3.23	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Chatterjee, Dutta et al.[1]	Accuracy	96.12	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Co-Tuning	Accuracy	85.65	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Physical Access	EER	5.74	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	riadd.aucmedi	AUROC	0.95	—	Unverified