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 2311–2320 of 10307 papers

Title	Date	Tasks	Status	Score
FuCiTNet: Improving the generalization of deep learning networks by the fusion of learned class-inherent transformations	May 17, 2020	Data AugmentationGeneral Classification	CodeCode Available	5
FUSE-ing Language Models: Zero-Shot Adapter Discovery for Prompt Optimization Across Tokenizers	Aug 9, 2024	Image CaptioningTransfer Learning	CodeCode Available	5
Context selectivity with dynamic availability enables lifelong continual learning	Jun 2, 2023	Continual LearningLifelong learning	CodeCode Available	5
GenSF: Simultaneous Adaptation of Generative Pre-trained Models and Slot Filling	Jun 13, 2021	Open-Domain Dialogslot-filling	CodeCode Available	5
Analyzing BERT Cross-lingual Transfer Capabilities in Continual Sequence Labeling	Oct 1, 2022	Continual LearningCross-Lingual Transfer	CodeCode Available	5
Auto-Transfer: Learning to Route Transferrable Representations	Feb 2, 2022	Transfer Learning	CodeCode Available	5
From Video Game to Real Robot: The Transfer between Action Spaces	May 2, 2019	Deep Reinforcement LearningReinforcement Learning	CodeCode Available	5
AutoTransfer: AutoML with Knowledge Transfer -- An Application to Graph Neural Networks	Mar 14, 2023	AutoMLTransfer Learning	CodeCode Available	5
From Colors to Classes: Emergence of Concepts in Vision Transformers	Mar 31, 2025	Domain AdaptationTransfer Learning	CodeCode Available	5
Auto-segmentation of Hip Joints using MultiPlanar UNet with Transfer learning	Aug 17, 2022	SegmentationTransfer 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