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 4681–4690 of 10307 papers

Title	Date	Tasks	Status
A comprehensive study on Blood Cancer detection and classification using Convolutional Neural Network	Sep 10, 2024	object-detectionObject Detection	—Unverified
A Study of Domain Generalization on Ultrasound-based Multi-Class Segmentation of Arteries, Veins, Ligaments, and Nerves Using Transfer Learning	Nov 13, 2020	Domain GeneralizationTransfer Learning	—Unverified
Learning for Cross-Layer Resource Allocation in MEC-Aided Cell-Free Networks	Dec 21, 2024	Edge-computingSelf-Supervised Learning	—Unverified
Learning from 2D: Contrastive Pixel-to-Point Knowledge Transfer for 3D Pretraining	Apr 10, 2021	Transfer Learning	—Unverified
Learning from a Neighbor: Adapting a Japanese Parser for Korean Through Feature Transfer Learning	Oct 1, 2014	Cross-Lingual TransferDependency Parsing	—Unverified
Learning from Auxiliary Sources in Argumentative Revision Classification	Sep 13, 2023	ClassificationMulti-Task Learning	—Unverified
Learning from Explanations and Demonstrations: A Pilot Study	Nov 1, 2020	text-to-speechText to Speech	—Unverified
Learning from Few Examples: A Summary of Approaches to Few-Shot Learning	Mar 7, 2022	Few-Shot LearningMeta-Learning	—Unverified
Learning from flowsheets: A generative transformer model for autocompletion of flowsheets	Aug 1, 2022	Causal Language ModelingLanguage Modeling	—Unverified
Learning from Higher-Layer Feature Visualizations	Mar 6, 2019	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