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 4101–4110 of 10307 papers

Title	Date	Tasks	Status	Hype
Self-Improving Safety Performance of Reinforcement Learning Based Driving with Black-Box Verification Algorithms	Oct 29, 2022	Autonomous DrivingDiversity	CodeCode Available	1
Space-Time Graph Neural Networks with Stochastic Graph Perturbations	Oct 28, 2022	Transfer Learning	—Unverified	0
Subsidiary Prototype Alignment for Universal Domain Adaptation	Oct 28, 2022	Domain AdaptationObject Recognition	—Unverified	0
Parameter-efficient transfer learning of pre-trained Transformer models for speaker verification using adapters	Oct 28, 2022	Speaker VerificationTransfer Learning	—Unverified	0
A Survey on Causal Representation Learning and Future Work for Medical Image Analysis	Oct 28, 2022	Causal InferenceMedical Image Analysis	CodeCode Available	0
Joint Semantic Transfer Network for IoT Intrusion Detection	Oct 28, 2022	Computational EfficiencyDomain Adaptation	—Unverified	0
TAD: Transfer Learning-based Multi-Adversarial Detection of Evasion Attacks against Network Intrusion Detection Systems	Oct 27, 2022	Intrusion DetectionNetwork Intrusion Detection	—Unverified	0
Drug repositioning for Alzheimer's disease with transfer learning	Oct 27, 2022	Deep LearningDrug Discovery	—Unverified	0
Towards High-Quality Neural TTS for Low-Resource Languages by Learning Compact Speech Representations	Oct 27, 2022	Transfer Learning	CodeCode Available	1
Multi-Target Tracking with Transferable Convolutional Neural Networks	Oct 27, 2022	Transfer 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