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 2421–2430 of 10307 papers

Title	Date	Tasks	Status	Hype
GYM at Qur’an QA 2023 Shared Task: Multi-Task Transfer Learning for Quranic Passage Retrieval and Question Answering with Large Language Models	Dec 7, 2023	Multi-Task LearningPassage Retrieval	CodeCode Available	0
Small Area Estimation of Case Growths for Timely COVID-19 Outbreak Detection	Dec 7, 2023	Transfer Learning	CodeCode Available	0
TLCE: Transfer-Learning Based Classifier Ensembles for Few-Shot Class-Incremental Learning	Dec 7, 2023	class-incremental learningClass Incremental Learning	—Unverified	0
Decoding Working-Memory Load During n-Back Task Performance from High Channel NIRS Data	Dec 6, 2023	Transfer Learning	—Unverified	0
A Scalable and Generalizable Pathloss Map Prediction	Dec 6, 2023	PredictionTransfer Learning	CodeCode Available	1
Does Vector Quantization Fail in Spatio-Temporal Forecasting? Exploring a Differentiable Sparse Soft-Vector Quantization Approach	Dec 6, 2023	AttributeComputational Efficiency	CodeCode Available	1
Customizable Combination of Parameter-Efficient Modules for Multi-Task Learning	Dec 6, 2023	Multi-Task Learningparameter-efficient fine-tuning	—Unverified	0
Optimizing Two-Pass Cross-Lingual Transfer Learning: Phoneme Recognition and Phoneme to Grapheme Translation	Dec 6, 2023	Cross-Lingual TransferPhoneme Recognition	—Unverified	0
Parameter-Efficient Transfer Learning of Audio Spectrogram Transformers	Dec 6, 2023	Audio ClassificationFew-Shot Learning	CodeCode Available	1
Similarity-based Knowledge Transfer for Cross-Domain Reinforcement Learning	Dec 5, 2023	MuJoCoreinforcement-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