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 2681–2690 of 10307 papers

Title	Date	Tasks	Status	Score
An Embarrassingly Simple Approach for Knowledge Distillation	Dec 5, 2018	Face RecognitionKnowledge Distillation	CodeCode Available	5
AutoTransfer: AutoML with Knowledge Transfer -- An Application to Graph Neural Networks	Mar 14, 2023	AutoMLTransfer Learning	CodeCode Available	5
Federated Continual Learning for Text Classification via Selective Inter-client Transfer	Oct 12, 2022	Continual LearningFederated Learning	CodeCode Available	5
Auto-Transfer: Learning to Route Transferrable Representations	Feb 2, 2022	Transfer Learning	CodeCode Available	5
Few-shot calibration of low-cost air pollution (PM2.5) sensors using meta-learning	Aug 2, 2021	Meta-LearningTransfer Learning	CodeCode Available	5
Fast Solar Image Classification Using Deep Learning and its Importance for Automation in Solar Physics	May 31, 2019	General Classificationimage-classification	CodeCode Available	5
FBDNN: Filter Banks and Deep Neural Networks for Portable and Fast Brain-Computer Interfaces	Sep 5, 2021	ClassificationEEG	CodeCode Available	5
FBK-DH at SemEval-2020 Task 12: Using Multi-channel BERT for Multilingual Offensive Language Detection	Dec 1, 2020	Language IdentificationMachine Translation	CodeCode Available	5
A Common Semantic Space for Monolingual and Cross-Lingual Meta-Embeddings	Jan 17, 2020	Cross-Lingual TransferPOS	CodeCode Available	5
Two-Level Attention-based Fusion Learning for RGB-D Face Recognition	Feb 29, 2020	Face RecognitionTransfer Learning	CodeCode Available	5

Show:10 25 50

← PrevPage 269 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