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 3561–3570 of 10307 papers

Title	Date	Tasks	Status	Hype
Denoising Diffusion Autoencoders are Unified Self-supervised Learners	Mar 17, 2023	Contrastive LearningDenoising	CodeCode Available	1
LION: Implicit Vision Prompt Tuning	Mar 17, 2023	Prompt LearningTransfer Learning	—Unverified	0
Efficient Computation Sharing for Multi-Task Visual Scene Understanding	Mar 16, 2023	Multi-Task LearningScene Understanding	CodeCode Available	0
Neural Architecture Search for Effective Teacher-Student Knowledge Transfer in Language Models	Mar 16, 2023	CoLACPU	—Unverified	0
Learning for Amalgamation: A Multi-Source Transfer Learning Framework For Sentiment Classification	Mar 16, 2023	Sentiment AnalysisSentiment Classification	CodeCode Available	0
Knowledge Transfer for Pseudo-code Generation from Low Resource Programming Language	Mar 16, 2023	Code GenerationDecoder	—Unverified	0
Imitation and Transfer Learning for LQG Control	Mar 16, 2023	Transfer Learning	—Unverified	0
Deep Metric Learning for Unsupervised Remote Sensing Change Detection	Mar 16, 2023	Change DetectionDisaster Response	CodeCode Available	1
Patch-Prompt Aligned Bayesian Prompt Tuning for Vision-Language Models	Mar 16, 2023	Prompt EngineeringTransfer Learning	—Unverified	0
A Survey of Deep Visual Cross-Domain Few-Shot Learning	Mar 16, 2023	Cross-Domain Few-Shotcross-domain few-shot learning	—Unverified	0

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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