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 3171–3180 of 10307 papers

Title	Date	Tasks	Status	Score
Automated Classification of Histopathology Images Using Transfer Learning	Mar 24, 2019	ClassificationDiagnostic	CodeCode Available	5
Domain Generalization by Marginal Transfer Learning	Nov 21, 2017	Domain GeneralizationGeneral Classification	CodeCode Available	5
Explicit Inductive Bias for Transfer Learning with Convolutional Networks	Feb 5, 2018	Inductive BiasTransfer Learning	CodeCode Available	5
Explainable Action Advising for Multi-Agent Reinforcement Learning	Nov 15, 2022	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	5
Explaining the physics of transfer learning a data-driven subgrid-scale closure to a different turbulent flow	Jun 7, 2022	Out-of-Distribution GeneralizationTransfer Learning	CodeCode Available	5
Uncovering the Hidden Cost of Model Compression	Aug 29, 2023	modelModel Compression	CodeCode Available	5
Exploiting Graph Structured Cross-Domain Representation for Multi-Domain Recommendation	Feb 12, 2023	Multi-Domain Recommender SystemsRecommendation Systems	CodeCode Available	5
Commonsense Knowledge Base Completion with Structural and Semantic Context	Oct 7, 2019	Computational EfficiencyKnowledge Base Completion	CodeCode Available	5
Expanding the Horizon: Enabling Hybrid Quantum Transfer Learning for Long-Tailed Chest X-Ray Classification	Apr 30, 2024	Binary ClassificationClassification	CodeCode Available	5
EXPANSE: A Deep Continual / Progressive Learning System for Deep Transfer Learning	May 19, 2022	Continual LearningTransfer 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