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 3791–3800 of 10307 papers

Title	Date	Tasks	Status
M&M3D: Multi-Dataset Training and Efficient Network for Multi-view 3D Object Detection	Nov 2, 2023	3D Object DetectionDomain Adaptation	CodeCode Available
ZEETAD: Adapting Pretrained Vision-Language Model for Zero-Shot End-to-End Temporal Action Detection	Nov 1, 2023	Action DetectionClassification	—Unverified
TLMCM Network for Medical Image Hierarchical Multi-Label Classification	Nov 1, 2023	ClassificationHierarchical Multi-label Classification	—Unverified
Transfer learning for improved generalizability in causal physics-informed neural networks for beam simulations	Nov 1, 2023	Transfer Learning	—Unverified
Dynamically Updating Event Representations for Temporal Relation Classification with Multi-category Learning	Oct 31, 2023	Multi-Task LearningRelation	—Unverified
Investigating Relative Performance of Transfer and Meta Learning	Oct 31, 2023	Meta-LearningTransfer Learning	—Unverified
Self-Supervised Pre-Training for Precipitation Post-Processor	Oct 31, 2023	Transfer Learning	—Unverified
MENTOR: Human Perception-Guided Pretraining for Increased Generalization	Oct 30, 2023	Anomaly DetectionDecoder	—Unverified
Topological Learning for Motion Data via Mixed Coordinates	Oct 30, 2023	Time SeriesTransfer Learning	CodeCode Available
On consequences of finetuning on data with highly discriminative features	Oct 30, 2023	Transfer Learning	—Unverified

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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