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 2581–2590 of 10307 papers

Title	Date	Tasks	Status	Hype
Graph Neural Networks for Road Safety Modeling: Datasets and Evaluations for Accident Analysis	Oct 31, 2023	Transfer Learning	CodeCode Available	1
Dynamically Updating Event Representations for Temporal Relation Classification with Multi-category Learning	Oct 31, 2023	Multi-Task LearningRelation	—Unverified	0
Investigating Relative Performance of Transfer and Meta Learning	Oct 31, 2023	Meta-LearningTransfer Learning	—Unverified	0
Self-Supervised Pre-Training for Precipitation Post-Processor	Oct 31, 2023	Transfer Learning	—Unverified	0
DDAM-PS: Diligent Domain Adaptive Mixer for Person Search	Oct 31, 2023	Domain AdaptationPedestrian Detection	CodeCode Available	1
Promise:Prompt-driven 3D Medical Image Segmentation Using Pretrained Image Foundation Models	Oct 30, 2023	Image SegmentationMedical Image Segmentation	CodeCode Available	1
Label-Only Model Inversion Attacks via Knowledge Transfer	Oct 30, 2023	modelTransfer Learning	CodeCode Available	1
On consequences of finetuning on data with highly discriminative features	Oct 30, 2023	Transfer Learning	—Unverified	0
AMLNet: Adversarial Mutual Learning Neural Network for Non-AutoRegressive Multi-Horizon Time Series Forecasting	Oct 30, 2023	DecoderDiversity	CodeCode Available	0
Topological Learning for Motion Data via Mixed Coordinates	Oct 30, 2023	Time SeriesTransfer Learning	CodeCode Available	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