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 881–890 of 10307 papers

Title	Date	Tasks	Status	Hype
Learning Multiple Adverse Weather Removal via Two-Stage Knowledge Learning and Multi-Contrastive Regularization: Toward a Unified Model	Jan 1, 2022	Transfer Learning	CodeCode Available	1
Learning Relation Prototype from Unlabeled Texts for Long-tail Relation Extraction	Nov 27, 2020	RelationRelation Extraction	CodeCode Available	1
Association Graph Learning for Multi-Task Classification with Category Shifts	Oct 10, 2022	General ClassificationGraph Learning	CodeCode Available	1
Learning to Adapt to Evolving Domains	Dec 1, 2020	Domain AdaptationMeta-Learning	CodeCode Available	1
Learning to Localize Actions from Moments	Aug 31, 2020	Action LocalizationTransfer Learning	CodeCode Available	1
Learning to Prompt for Open-Vocabulary Object Detection with Vision-Language Model	Mar 28, 2022	image-classificationImage Classification	CodeCode Available	1
Adversarial Self-Supervised Contrastive Learning	Jun 13, 2020	Adversarial AttackContrastive Learning	CodeCode Available	1
Learning the Travelling Salesperson Problem Requires Rethinking Generalization	Jun 12, 2020	Combinatorial OptimizationTransfer Learning	CodeCode Available	1
Learning with Alignments: Tackling the Inter- and Intra-domain Shifts for Cross-multidomain Facial Expression Recognition	Jul 8, 2024	Facial Expression RecognitionFacial Expression Recognition (FER)	CodeCode Available	1
MutualNet: Adaptive ConvNet via Mutual Learning from Network Width and Resolution	Sep 27, 2019	GPUInstance Segmentation	CodeCode Available	1

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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