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 4781–4790 of 10307 papers

Title	Date	Tasks	Status
A Survey of Multilingual Models for Automatic Speech Recognition	Feb 25, 2022	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Learning to Selectively Transfer: Reinforced Transfer Learning for Deep Text Matching	Dec 30, 2018	Information RetrievalNatural Language Inference	—Unverified
Learning to Select Pre-Trained Deep Representations with Bayesian Evidence Framework	Jun 8, 2015	Transfer Learning	—Unverified
Learning to Teach Reinforcement Learning Agents	Jul 28, 2017	reinforcement-learningReinforcement Learning	—Unverified
Learning to Transfer	Aug 18, 2017	Transfer Learning	—Unverified
Learning to Transfer Dynamic Models of Underactuated Soft Robotic Hands	May 21, 2020	Transfer Learning	—Unverified
Learning to Transfer for Evolutionary Multitasking	Jun 20, 2024	Evolutionary AlgorithmsTransfer Learning	—Unverified
Learning to Transfer Graph Embeddings for Inductive Graph based Recommendation	May 24, 2020	Graph Neural NetworkTransfer Learning	—Unverified
Learning to Transfer Learn: Reinforcement Learning-Based Selection for Adaptive Transfer Learning	Aug 29, 2019	reinforcement-learningReinforcement Learning	—Unverified
Learning to Transfer: Transferring Latent Task Structures and Its Application to Person-Specific Facial Action Unit Detection	Dec 1, 2015	Action Unit DetectionFacial Action Unit Detection	—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