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 721–730 of 10307 papers

Title	Date	Tasks	Status	Hype
FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image Classification	Jun 17, 2022	Federated LearningFew-Shot Learning	CodeCode Available	1
CtrlFormer: Learning Transferable State Representation for Visual Control via Transformer	Jun 17, 2022	Transfer Learning	CodeCode Available	1
Time Interval-enhanced Graph Neural Network for Shared-account Cross-domain Sequential Recommendation	Jun 16, 2022	Graph Neural NetworkRepresentation Learning	CodeCode Available	1
CARLANE: A Lane Detection Benchmark for Unsupervised Domain Adaptation from Simulation to multiple Real-World Domains	Jun 16, 2022	2D Semantic SegmentationAutonomous Driving	CodeCode Available	1
Hybrid thermal modeling of additive manufacturing processes using physics-informed neural networks for temperature prediction and parameter identification	Jun 15, 2022	Transfer Learning	CodeCode Available	1
Evaluating histopathology transfer learning with ChampKit	Jun 14, 2022	BenchmarkingCell Detection	CodeCode Available	1
The Modality Focusing Hypothesis: Towards Understanding Crossmodal Knowledge Distillation	Jun 13, 2022	Knowledge DistillationTransfer Learning	CodeCode Available	1
APT-36K: A Large-scale Benchmark for Animal Pose Estimation and Tracking	Jun 12, 2022	Animal Pose EstimationDomain Generalization	CodeCode Available	1
Toward Real-world Single Image Deraining: A New Benchmark and Beyond	Jun 11, 2022	Domain GeneralizationImage Restoration	CodeCode Available	1
CFA: Coupled-hypersphere-based Feature Adaptation for Target-Oriented Anomaly Localization	Jun 9, 2022	Anomaly DetectionAnomaly Localization	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