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 1741–1750 of 10307 papers

Title	Date	Tasks	Status	Score
HintNet: Hierarchical Knowledge Transfer Networks for Traffic Accident Forecasting on Heterogeneous Spatio-Temporal Data	Mar 7, 2022	ManagementTransfer Learning	CodeCode Available	5
Action Priors for Large Action Spaces in Robotics	Jan 11, 2021	reinforcement-learningReinforcement Learning (RL)	CodeCode Available	5
CBM: Curriculum by Masking	Jul 6, 2024	Object RecognitionTransfer Learning	CodeCode Available	5
Histogram-based Parameter-efficient Tuning for Passive Sonar Classification	Apr 21, 2025	Transfer Learning	CodeCode Available	5
How Language-Neutral is Multilingual BERT?	Nov 8, 2019	RetrievalSentence	CodeCode Available	5
Adversarial Data Programming: Using GANs to Relax the Bottleneck of Curated Labeled Data	Mar 14, 2018	Multi-Task LearningTransfer Learning	CodeCode Available	5
An Unsupervised Multiple-Task and Multiple-Teacher Model for Cross-lingual Named Entity Recognition	Nov 16, 2021	Cross-Lingual NERKnowledge Distillation	CodeCode Available	5
Chest X-Ray Images Classification with CNN	Dec 31, 2022	ClassificationDiagnostic	CodeCode Available	5
Bayesian Meta-Learning for Improving Generalizability of Health Prediction Models With Similar Causal Mechanisms	Oct 19, 2023	Meta-LearningMulti-Task Learning	CodeCode Available	5
Privacy-Aware Lifelong Learning	May 16, 2025	image-classificationImage Classification	CodeCode Available	5

Show:10 25 50

← PrevPage 175 of 1031Next →

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