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 2841–2850 of 10307 papers

Title	Date	Tasks	Status	Hype
Generalized Cross-domain Multi-label Few-shot Learning for Chest X-rays	Sep 8, 2023	Few-Shot LearningMeta-Learning	—Unverified	0
Overcoming Data Limitations: A Few-Shot Specific Emitter Identification Method Using Self-Supervised Learning and Adversarial Augmentation	Sep 7, 2023	Self-Supervised LearningTransfer Learning	CodeCode Available	1
Enabling Intelligent Vehicular Networks Through Distributed Learning in the Non-Terrestrial Networks 6G Vision	Sep 7, 2023	Edge-computingFederated Learning	—Unverified	0
S-Adapter: Generalizing Vision Transformer for Face Anti-Spoofing with Statistical Tokens	Sep 7, 2023	Domain GeneralizationFace Anti-Spoofing	—Unverified	0
Leveraging ASR Pretrained Conformers for Speaker Verification through Transfer Learning and Knowledge Distillation	Sep 6, 2023	Knowledge DistillationSpeaker Verification	—Unverified	0
Active shooter detection and robust tracking utilizing supplemental synthetic data	Sep 6, 2023	Transfer Learning	—Unverified	0
EvoCLINICAL: Evolving Cyber-Cyber Digital Twin with Active Transfer Learning for Automated Cancer Registry System	Sep 6, 2023	Active LearningTransfer Learning	CodeCode Available	0
Adaptive Growth: Real-time CNN Layer Expansion	Sep 6, 2023	Transfer Learning	CodeCode Available	0
Roulette: A Semantic Privacy-Preserving Device-Edge Collaborative Inference Framework for Deep Learning Classification Tasks	Sep 6, 2023	Collaborative InferencePrivacy Preserving	—Unverified	0
Probabilistic Self-supervised Learning via Scoring Rules Minimization	Sep 5, 2023	Knowledge DistillationOut-of-Distribution Detection	—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