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 3061–3070 of 10307 papers

Title	Date	Tasks	Status
Transfer Learning with Informative Priors: Simple Baselines Better than Previously Reported	May 24, 2024	Transfer Learning	CodeCode Available
The Impact of Geometric Complexity on Neural Collapse in Transfer Learning	May 24, 2024	Transfer Learning	—Unverified
SF-DQN: Provable Knowledge Transfer using Successor Feature for Deep Reinforcement Learning	May 24, 2024	Deep Reinforcement LearningQ-Learning	—Unverified
Combining Denoising Autoencoders with Contrastive Learning to fine-tune Transformer Models	May 23, 2024	Contrastive LearningData Augmentation	CodeCode Available
An LSTM Feature Imitation Network for Hand Movement Recognition from sEMG Signals	May 23, 2024	Feature EngineeringTransfer Learning	CodeCode Available
Magnetic Resonance Image Processing Transformer for General Accelerated Image Reconstruction	May 23, 2024	AnatomyDeep Learning	—Unverified
Federated Domain-Specific Knowledge Transfer on Large Language Models Using Synthetic Data	May 23, 2024	Federated LearningTransfer Learning	—Unverified
Multilingual Prosody Transfer: Comparing Supervised & Transfer Learning	May 23, 2024	Speech Synthesistext-to-speech	—Unverified
Deep learning lattice gauge theories	May 23, 2024	Deep LearningTransfer Learning	—Unverified
MAMOC: MRI Motion Correction via Masked Autoencoding	May 23, 2024	Transfer Learning	—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