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 4281–4290 of 10307 papers

Title	Date	Tasks	Status
CVAE-based Re-anchoring for Implicit Discourse Relation Classification	Nov 1, 2021	Binary ClassificationClassification	—Unverified
Augmenting Transfer Learning with Semantic Reasoning	May 31, 2019	Transfer Learning	—Unverified
AMLN: Adversarial-based Mutual Learning Network for Online Knowledge Distillation	Aug 1, 2020	Knowledge DistillationTransfer Learning	—Unverified
Customizing General-Purpose Foundation Models for Medical Report Generation	Jun 9, 2023	Medical Report GenerationTransfer Learning	—Unverified
Handling Variable-Dimensional Time Series with Graph Neural Networks	Jul 1, 2020	Activity RecognitionGraph Neural Network	—Unverified
Customizing Contextualized Language Models forLegal Document Reviews	Feb 10, 2021	Language ModelingLanguage Modelling	—Unverified
Customized Video QoE Estimation with Algorithm-Agnostic Transfer Learning	Mar 12, 2020	BIG-bench Machine LearningDiversity	—Unverified
A Minimax Game for Instance based Selective Transfer Learning	Jul 1, 2019	RetrievalText Retrieval	—Unverified
Customizable Combination of Parameter-Efficient Modules for Multi-Task Learning	Dec 6, 2023	Multi-Task Learningparameter-efficient fine-tuning	—Unverified
Curriculum Learning in Deep Neural Networks for Financial Forecasting	Apr 29, 2019	DecoderTime Series	—Unverified

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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