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 2511–2520 of 10307 papers

Title	Date	Tasks	Status	Score
Few-Shot Out-of-Domain Transfer Learning of Natural Language Explanations in a Label-Abundant Setup	Dec 12, 2021	Natural Language InferenceTransfer Learning	CodeCode Available	5
Few-Shot Image Recognition With Knowledge Transfer	Oct 1, 2019	Transfer Learning	CodeCode Available	5
3D-PointZshotS: Geometry-Aware 3D Point Cloud Zero-Shot Semantic Segmentation Narrowing the Visual-Semantic Gap	Apr 16, 2025	Point Cloud SegmentationSemantic Segmentation	CodeCode Available	5
Feudal Graph Reinforcement Learning	Apr 11, 2023	Decision MakingGraph Clustering	CodeCode Available	5
Few-shot calibration of low-cost air pollution (PM2.5) sensors using meta-learning	Aug 2, 2021	Meta-LearningTransfer Learning	CodeCode Available	5
Data-Efficient Image Recognition with Contrastive Predictive Coding	May 22, 2019	Contrastive LearningGeneral Classification	CodeCode Available	5
FedRef: Communication-Efficient Bayesian Fine Tuning with Reference Model	Jun 29, 2025	Brain Tumor SegmentationFederated Learning	CodeCode Available	5
Gotta Learn Fast: A New Benchmark for Generalization in RL	Apr 10, 2018	Few-Shot Learningreinforcement-learning	CodeCode Available	5
Cross-View Policy Learning for Street Navigation	Jun 13, 2019	Deep Reinforcement LearningNavigate	CodeCode Available	5
Few-shot classification in Named Entity Recognition Task	Dec 14, 2018	ClassificationGeneral Classification	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