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 4121–4130 of 10307 papers

Title	Date	Tasks	Status	Hype
Embedding neurophysiological signals	Oct 25, 2022	Brain Computer InterfaceDomain Adaptation	CodeCode Available	0
Empowering Long-tail Item Recommendation through Cross Decoupling Network (CDN)	Oct 25, 2022	MemorizationRecommendation Systems	—Unverified	0
Progressive Sentiment Analysis for Code-Switched Text Data	Oct 25, 2022	Cross-Lingual Transfernamed-entity-recognition	CodeCode Available	0
A single-cell gene expression language model	Oct 25, 2022	DiversityLanguage Modeling	CodeCode Available	1
FedClassAvg: Local Representation Learning for Personalized Federated Learning on Heterogeneous Neural Networks	Oct 25, 2022	Federated LearningPersonalized Federated Learning	CodeCode Available	1
CLIP-FLow: Contrastive Learning by semi-supervised Iterative Pseudo labeling for Optical Flow Estimation	Oct 25, 2022	Contrastive LearningOptical Flow Estimation	—Unverified	0
PALT: Parameter-Lite Transfer of Language Models for Knowledge Graph Completion	Oct 25, 2022	Knowledge Graph CompletionLink Prediction	CodeCode Available	1
XRICL: Cross-lingual Retrieval-Augmented In-Context Learning for Cross-lingual Text-to-SQL Semantic Parsing	Oct 25, 2022	In-Context LearningRetrieval	CodeCode Available	0
Accelerating Certified Robustness Training via Knowledge Transfer	Oct 25, 2022	Transfer Learning	CodeCode Available	0
This joke is [MASK]: Recognizing Humor and Offense with Prompting	Oct 25, 2022	Transfer Learning	—Unverified	0

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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