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 2081–2090 of 10307 papers

Title	Date	Tasks	Status
An Unified Search and Recommendation Foundation Model for Cold-Start Scenario	Sep 16, 2023	Recommendation SystemsTransfer Learning	—Unverified
Causal Reinforcement Learning: A Survey	Jul 4, 2023	reinforcement-learningReinforcement Learning	—Unverified
Adversarial Contrastive Distillation with Adaptive Denoising	Feb 17, 2023	Adversarial RobustnessDenoising	—Unverified
Causal Modeling in Multi-Context Systems: Distinguishing Multiple Context-Specific Causal Graphs which Account for Observational Support	Oct 27, 2024	Anomaly DetectionTransfer Learning	—Unverified
Causally Regularized Learning with Agnostic Data Selection Bias	Aug 22, 2017	regressionSelection bias	—Unverified
Cross-Subject Deep Transfer Models for Evoked Potentials in Brain-Computer Interface	Jan 29, 2023	BenchmarkingBrain Computer Interface	—Unverified
Cross-Subject Transfer Learning Improves the Practicality of Real-World Applications of Brain-Computer Interfaces	Oct 5, 2018	SSVEPSubject Transfer	—Unverified
An Uncertainty-Aware Deep Learning Framework for Defect Detection in Casting Products	Jul 24, 2021	Defect Detectionimage-classification	—Unverified
Transfer Learning for Individual Treatment Effect Estimation	Oct 1, 2022	Causal Inferencecounterfactual	—Unverified
Advantages of biologically-inspired adaptive neural activation in RNNs during learning	Jun 22, 2020	Transfer Learning	—Unverified

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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