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 3701–3710 of 10307 papers

Title	Date	Tasks	Status	Hype
Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement Learning	Feb 13, 2023	Deep Reinforcement Learningreinforcement-learning	CodeCode Available	1
Gradient-Based Automated Iterative Recovery for Parameter-Efficient Tuning	Feb 13, 2023	Decision MakingTransfer Learning	—Unverified	0
Label-efficient Time Series Representation Learning: A Review	Feb 13, 2023	Representation LearningSelf-Supervised Learning	—Unverified	0
LiT Tuned Models for Efficient Species Detection	Feb 12, 2023	Fine-Grained Image Classificationimage-classification	CodeCode Available	0
Exploiting Graph Structured Cross-Domain Representation for Multi-Domain Recommendation	Feb 12, 2023	Multi-Domain Recommender SystemsRecommendation Systems	CodeCode Available	0
Transfer Learning for Bayesian Optimization: A Survey	Feb 12, 2023	Bayesian OptimizationDrug Design	—Unverified	0
Vertical Federated Knowledge Transfer via Representation Distillation for Healthcare Collaboration Networks	Feb 11, 2023	DiagnosticFederated Learning	—Unverified	0
Cross-center Early Sepsis Recognition by Medical Knowledge Guided Collaborative Learning for Data-scarce Hospitals	Feb 11, 2023	Transfer Learning	—Unverified	0
Robust Knowledge Transfer in Tiered Reinforcement Learning	Feb 10, 2023	reinforcement-learningReinforcement Learning	CodeCode Available	0
Self-supervised learning-based cervical cytology for the triage of HPV-positive women in resource-limited settings and low-data regime	Feb 10, 2023	Multiple Instance LearningSelf-Supervised Learning	—Unverified	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