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 3541–3550 of 10307 papers

Title	Date	Tasks	Status
ClaRet -- A CNN Architecture for Optical Coherence Tomography	Nov 30, 2022	Transfer Learning	—Unverified
Enhancing Cross-domain Click-Through Rate Prediction via Explicit Feature Augmentation	Nov 30, 2023	Click-Through Rate PredictionTransfer Learning	—Unverified
Enhancing Cross-target Stance Detection with Transferable Semantic-Emotion Knowledge	Jul 1, 2020	Stance DetectionTransfer Learning	—Unverified
Enhancing CTR Prediction through Sequential Recommendation Pre-training: Introducing the SRP4CTR Framework	Jul 29, 2024	Click-Through Rate PredictionSelf-Supervised Learning	—Unverified
Enhancing Graph Neural Networks with Limited Labeled Data by Actively Distilling Knowledge from Large Language Models	Jul 19, 2024	Active LearningKnowledge Graphs	—Unverified
Class-based Subset Selection for Transfer Learning under Extreme Label Shift	Dec 30, 2024	Transfer Learning	—Unverified
Class Conditional Alignment for Partial Domain Adaptation	Mar 14, 2020	Domain AdaptationPartial Domain Adaptation	—Unverified
Enhancing ensemble learning and transfer learning in multimodal data analysis by adaptive dimensionality reduction	May 8, 2021	Dimensionality ReductionEnsemble Learning	—Unverified
Enhancing Entertainment Translation for Indian Languages using Adaptive Context, Style and LLMs	Dec 29, 2024	Large Language ModelMachine Translation	—Unverified
Deep Decomposition for Stochastic Normal-Abnormal Transport	Nov 29, 2021	Optical Flow EstimationTime 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