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 3021–3030 of 10307 papers

Title	Date	Tasks	Status	Score
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
COVID-19 Detection in Chest X-Ray Images using a New Channel Boosted CNN	Dec 8, 2020	Domain AdaptationTransfer Learning	CodeCode Available	5
Explicit Alignment Objectives for Multilingual Bidirectional Encoders	Oct 15, 2020	Cross-Lingual TransferRetrieval	CodeCode Available	5
Disease Knowledge Transfer across Neurodegenerative Diseases	Jan 11, 2019	Transfer Learning	CodeCode Available	5
Exclusive Supermask Subnetwork Training for Continual Learning	Oct 18, 2022	Continual LearningText Classification	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
Disentangled Contrastive Learning for Social Recommendation	Aug 18, 2022	Contrastive LearningRepresentation Learning	CodeCode Available	5
Explicit Inductive Bias for Transfer Learning with Convolutional Networks	Feb 5, 2018	Inductive BiasTransfer Learning	CodeCode Available	5
Exploring Large Language Models and Hierarchical Frameworks for Classification of Large Unstructured Legal Documents	Mar 11, 2024	ClusteringLanguage Modelling	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