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 4771–4780 of 10307 papers

Title	Date	Tasks	Status
Learning to Project for Cross-Task Knowledge Distillation	Mar 21, 2024	Depth EstimationKnowledge Distillation	—Unverified
A Survey of Learning on Small Data: Generalization, Optimization, and Challenge	Jul 29, 2022	Active LearningContrastive Learning	—Unverified
Learning to Rank based on Analogical Reasoning	Nov 28, 2017	Learning-To-RankObject	—Unverified
Learning to Rank Learning Curves	Jun 5, 2020	Learning-To-RankNeural Architecture Search	—Unverified
A Survey of Methods to Leverage Monolingual Data in Low-resource Neural Machine Translation	Oct 1, 2019	Low Resource Neural Machine TranslationLow-Resource Neural Machine Translation	—Unverified
Learning to Retain while Acquiring: Combating Distribution-Shift in Adversarial Data-Free Knowledge Distillation	Feb 28, 2023	Data-free Knowledge DistillationKnowledge Distillation	—Unverified
Learning to rumble: Automated elephant call classification, detection and endpointing using deep architectures	Oct 15, 2024	ClassificationManagement	—Unverified
Learning to search for and detect objects in foveal images using deep learning	Apr 12, 2023	Object LocalizationTransfer Learning	—Unverified
Learning to see across Domains and Modalities	Feb 13, 2019	Domain Adaptationimage-classification	—Unverified
Learning to See before Learning to Act: Visual Pre-training for Manipulation	Jul 1, 2021	Transfer Learning	—Unverified

Show:10 25 50

← PrevPage 478 of 1031Next →

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