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 4651–4660 of 10307 papers

Title	Date	Tasks	Status
Learning 4D Panoptic Scene Graph Generation from Rich 2D Visual Scene	Jan 1, 2025	Graph GenerationLarge Language Model	—Unverified
Learning Abstract Concept Embeddings from Multi-Modal Data: Since You Probably Can't See What I Mean	Oct 1, 2014	Transfer Learning	—Unverified
Learning across label confidence distributions using Filtered Transfer Learning	Jun 3, 2020	Transfer Learning	—Unverified
Assistive Completion of Agrammatic Aphasic Sentences: A Transfer Learning Approach using Neurolinguistics-based Synthetic Dataset	Nov 10, 2022	Semantic SimilaritySemantic Textual Similarity	—Unverified
Learning a Deep Compact Image Representation for Visual Tracking	Dec 1, 2013	DenoisingGeneral Classification	—Unverified
Learning a Deep Model for Human Action Recognition from Novel Viewpoints	Feb 2, 2016	Action RecognitionTemporal Action Localization	—Unverified
Assistive Diagnostic Tool for Brain Tumor Detection using Computer Vision	Nov 17, 2020	DiagnosticSegmentation	—Unverified
Learning a functional control for high-frequency finance	Jun 17, 2020	Transfer LearningVocal Bursts Intensity Prediction	—Unverified
Robust and flexible learning of a high-dimensional classification rule using auxiliary outcomes	Nov 11, 2020	ClassificationGeneral Classification	—Unverified
Learning and Knowledge Transfer with Memory Networks for Machine Comprehension	Apr 1, 2017	Question AnsweringReading Comprehension	—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