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 3591–3600 of 10307 papers

Title	Date	Tasks	Status	Hype
Transformer-based approaches to Sentiment Detection	Mar 13, 2023	text-classificationText Classification	—Unverified	0
Transferable Deep Learning Power System Short-Term Voltage Stability Assessment with Physics-Informed Topological Feature Engineering	Mar 13, 2023	Feature EngineeringTransfer Learning	—Unverified	0
Improving physics-informed neural networks with meta-learned optimization	Mar 13, 2023	Transfer Learning	—Unverified	0
Functional Knowledge Transfer with Self-supervised Representation Learning	Mar 12, 2023	Representation LearningSelf-Supervised Learning	CodeCode Available	0
Generalized 3D Self-supervised Learning Framework via Prompted Foreground-Aware Feature Contrast	Mar 11, 2023	3D Semantic SegmentationContrastive Learning	—Unverified	0
Software Vulnerability Prediction Knowledge Transferring Between Programming Languages	Mar 10, 2023	PredictionTransfer Learning	—Unverified	0
Overwriting Pretrained Bias with Finetuning Data	Mar 10, 2023	AttributeTransfer Learning	CodeCode Available	0
Optimizing Federated Learning for Medical Image Classification on Distributed Non-iid Datasets with Partial Labels	Mar 10, 2023	Federated Learningimage-classification	—Unverified	0
Scaling Up 3D Kernels with Bayesian Frequency Re-parameterization for Medical Image Segmentation	Mar 10, 2023	Computational EfficiencyImage Segmentation	CodeCode Available	1
HiNet: Novel Multi-Scenario & Multi-Task Learning with Hierarchical Information Extraction	Mar 10, 2023	Multi-Task LearningRecommendation Systems	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