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 4021–4030 of 10307 papers

Title	Date	Tasks	Status	Hype
NEVIS'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision Research	Nov 15, 2022	Continual LearningDiversity	CodeCode Available	1
A Point in the Right Direction: Vector Prediction for Spatially-aware Self-supervised Volumetric Representation Learning	Nov 15, 2022	Image SegmentationMedical Image Segmentation	—Unverified	0
SexWEs: Domain-Aware Word Embeddings via Cross-lingual Semantic Specialisation for Chinese Sexism Detection in Social Media	Nov 15, 2022	Cross-Lingual TransferTransfer Learning	CodeCode Available	0
FedTune: A Deep Dive into Efficient Federated Fine-Tuning with Pre-trained Transformers	Nov 15, 2022	Federated LearningLanguage Modelling	—Unverified	0
Explainable Action Advising for Multi-Agent Reinforcement Learning	Nov 15, 2022	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	0
Using Human Perception to Regularize Transfer Learning	Nov 15, 2022	Transfer Learning	—Unverified	0
DIGEST: Deeply supervIsed knowledGE tranSfer neTwork learning for brain tumor segmentation with incomplete multi-modal MRI scans	Nov 15, 2022	Brain Tumor SegmentationImage Segmentation	—Unverified	0
Improving ECG-based COVID-19 diagnosis and mortality predictions using pre-pandemic medical records at population-scale	Nov 14, 2022	COVID-19 DiagnosisDiagnostic	—Unverified	0
Renewing Iterative Self-labeling Domain Adaptation with Application to the Spine Motion Prediction	Nov 14, 2022	Domain Adaptationmotion prediction	—Unverified	0
Federated Learning for Autoencoder-based Condition Monitoring in the Industrial Internet of Things	Nov 14, 2022	Federated LearningTransfer 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