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 3451–3460 of 10307 papers

Title	Date	Tasks	Status	Hype
RECLIP: Resource-efficient CLIP by Training with Small Images	Apr 12, 2023	Contrastive LearningImage-text Retrieval	—Unverified	0
Learning to search for and detect objects in foveal images using deep learning	Apr 12, 2023	Object LocalizationTransfer Learning	—Unverified	0
Model Sparsity Can Simplify Machine Unlearning	Apr 11, 2023	Machine Unlearningmodel	CodeCode Available	1
Prediction of brain tumor recurrence location based on multi-modal fusion and nonlinear correlation learning	Apr 11, 2023	Brain Tumor SegmentationTransfer Learning	—Unverified	0
Conditional Adapters: Parameter-efficient Transfer Learning with Fast Inference	Apr 11, 2023	Transfer Learning	—Unverified	0
A Self-attention Knowledge Domain Adaptation Network for Commercial Lithium-ion Batteries State-of-health Estimation under Shallow Cycles	Apr 11, 2023	Domain AdaptationTransfer Learning	—Unverified	0
Feudal Graph Reinforcement Learning	Apr 11, 2023	Decision MakingGraph Clustering	CodeCode Available	0
A Deep Analysis of Transfer Learning Based Breast Cancer Detection Using Histopathology Images	Apr 11, 2023	Breast Cancer DetectionTransfer Learning	—Unverified	0
Automaton-Guided Curriculum Generation for Reinforcement Learning Agents	Apr 11, 2023	Decision MakingQ-Learning	CodeCode Available	0
Transfer Learning Across Heterogeneous Features For Efficient Tensor Program Generation	Apr 11, 2023	Transfer Learning	CodeCode Available	0

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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