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 1751–1760 of 10307 papers

Title	Date	Tasks	Status	Score
ICICLE: Interpretable Class Incremental Continual Learning	Mar 14, 2023	class-incremental learningClass Incremental Learning	CodeCode Available	5
Identification of head impact locations, speeds, and force based on head kinematics	Sep 12, 2024	Transfer Learning	CodeCode Available	5
HintNet: Hierarchical Knowledge Transfer Networks for Traffic Accident Forecasting on Heterogeneous Spatio-Temporal Data	Mar 7, 2022	ManagementTransfer Learning	CodeCode Available	5
Choice Fusion as Knowledge for Zero-Shot Dialogue State Tracking	Feb 25, 2023	DecoderDialogue State Tracking	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
HistoKT: Cross Knowledge Transfer in Computational Pathology	Jan 27, 2022	Transfer Learning	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
CIFAR-10 Image Classification Using Feature Ensembles	Feb 7, 2020	ClassificationGeneral Classification	CodeCode Available	5
Privacy-Aware Lifelong Learning	May 16, 2025	image-classificationImage Classification	CodeCode Available	5
Hindi/Bengali Sentiment Analysis Using Transfer Learning and Joint Dual Input Learning with Self Attention	Feb 11, 2022	Sentiment AnalysisTransfer Learning	CodeCode Available	5

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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