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 3001–3010 of 10307 papers

Title	Date	Tasks	Status	Score
On Generalizing Detection Models for Unconstrained Environments	Sep 28, 2019	Incremental LearningObject	CodeCode Available	5
Dimensionless Policies based on the Buckingham π Theorem: Is This a Good Way to Generalize Numerical Results?	Jul 29, 2023	Transfer Learning	CodeCode Available	5
Exploiting Graph Structured Cross-Domain Representation for Multi-Domain Recommendation	Feb 12, 2023	Multi-Domain Recommender SystemsRecommendation Systems	CodeCode Available	5
EXPANSE: A Deep Continual / Progressive Learning System for Deep Transfer Learning	May 19, 2022	Continual LearningTransfer Learning	CodeCode Available	5
Expert-Free Online Transfer Learning in Multi-Agent Reinforcement Learning	Jan 26, 2025	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	5
3D_DEN: Open-ended 3D Object Recognition using Dynamically Expandable Networks	Sep 15, 2020	3D Object RecognitionContinual Learning	CodeCode Available	5
COVID-19 Detection Using Transfer Learning Approach from Computed Tomography Images	Jul 1, 2022	Computed Tomography (CT)COVID-19 Diagnosis	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
On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task	Jul 6, 2017	3D Medical Imaging SegmentationImage Segmentation	CodeCode Available	5
Explainable Action Advising for Multi-Agent Reinforcement Learning	Nov 15, 2022	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	5

Show:10 25 50

← PrevPage 301 of 1031Next →

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