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 3361–3370 of 10307 papers

Title	Date	Tasks	Status	Hype
PointCMP: Contrastive Mask Prediction for Self-supervised Learning on Point Cloud Videos	May 6, 2023	Self-Supervised LearningTransfer Learning	CodeCode Available	1
Cross-domain Augmentation Networks for Click-Through Rate Prediction	May 6, 2023	Click-Through Rate PredictionPrediction	—Unverified	0
Online Gesture Recognition using Transformer and Natural Language Processing	May 5, 2023	DecoderGesture Recognition	—Unverified	0
Knowledge Transfer from Teachers to Learners in Growing-Batch Reinforcement Learning	May 5, 2023	Decision Makingreinforcement-learning	—Unverified	0
Towards Effective Collaborative Learning in Long-Tailed Recognition	May 5, 2023	Transfer Learning	—Unverified	0
An Imitation Learning Based Algorithm Enabling Priori Knowledge Transfer in Modern Electricity Markets for Bayesian Nash Equilibrium Estimation	May 4, 2023	Imitation LearningTransfer Learning	—Unverified	0
Semi-supervised Domain Adaptation via Prototype-based Multi-level Learning	May 4, 2023	Domain AdaptationPseudo Label	CodeCode Available	1
Emulation Learning for Neuromimetic Systems	May 4, 2023	Model Predictive ControlQuantization	—Unverified	0
Avatar Knowledge Distillation: Self-ensemble Teacher Paradigm with Uncertainty	May 4, 2023	Knowledge Distillationobject-detection	CodeCode Available	1
Shotgun crystal structure prediction using machine-learned formation energies	May 3, 2023	PredictionTransfer Learning	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