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 4501–4510 of 10307 papers

Title	Date	Tasks	Status	Hype
Transfer learning for time series classification using synthetic data generation	Jul 16, 2022	Classificationregression	CodeCode Available	1
Convolutional Bypasses Are Better Vision Transformer Adapters	Jul 14, 2022	Few-Shot LearningInductive Bias	CodeCode Available	1
Current Trends in Deep Learning for Earth Observation: An Open-source Benchmark Arena for Image Classification	Jul 14, 2022	ClassificationEarth Observation	CodeCode Available	2
DEXTER: An end-to-end system to extract table contents from electronic medical health documents	Jul 14, 2022	Cell DetectionOptical Character Recognition (OCR)	—Unverified	0
A Deep Learning-Based GPR Forward Solver for Predicting B-Scans of Subsurface Objects	Jul 13, 2022	DecoderGPR	—Unverified	0
A Transfer Learning Based Model for Text Readability Assessment in German	Jul 13, 2022	Language ModelingLanguage Modelling	—Unverified	0
Continual Meta-Reinforcement Learning for UAV-Aided Vehicular Wireless Networks	Jul 13, 2022	Meta Reinforcement Learningreinforcement-learning	—Unverified	0
On the Generalization for Transfer Learning: An Information-Theoretic Analysis	Jul 12, 2022	Domain AdaptationTransfer Learning	—Unverified	0
RE-Tagger: A light-weight Real-Estate Image Classifier	Jul 12, 2022	image-classificationImage Classification	—Unverified	0
HEAD: HEtero-Assists Distillation for Heterogeneous Object Detectors	Jul 12, 2022	Knowledge DistillationObject	CodeCode Available	1

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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