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	Hype
Model-based Transfer Learning for Automatic Optical Inspection based on domain discrepancy	Jan 14, 2023	Data AugmentationTransfer Learning	CodeCode Available	0
Discovery of 2D materials using Transformer Network based Generative Design	Jan 14, 2023	Formation EnergySelf-Learning	CodeCode Available	2
FUN with Fisher: Improving Generalization of Adapter-Based Cross-lingual Transfer with Scheduled Unfreezing	Jan 13, 2023	Cross-Lingual TransferTransfer Learning	CodeCode Available	0
Efficient and robust transfer learning of optimal individualized treatment regimes with right-censored survival data	Jan 13, 2023	counterfactualparameter estimation	CodeCode Available	0
TransfQMix: Transformers for Leveraging the Graph Structure of Multi-Agent Reinforcement Learning Problems	Jan 13, 2023	Multi-agent Reinforcement LearningQ-Learning	CodeCode Available	1
Development of a Prototype Application for Rice Disease Detection Using Convolutional Neural Networks	Jan 13, 2023	Image AugmentationTransfer Learning	—Unverified	0
Sim2real Transfer Learning for Point Cloud Segmentation: An Industrial Application Case on Autonomous Disassembly	Jan 12, 2023	Point Cloud SegmentationTransfer Learning	—Unverified	0
Language-Informed Transfer Learning for Embodied Household Activities	Jan 12, 2023	Semantic SimilaritySemantic Textual Similarity	—Unverified	0
Switchable Lightweight Anti-symmetric Processing (SLAP) with CNN Outspeeds Data Augmentation by Smaller Sample -- Application in Gomoku Reinforcement Learning	Jan 11, 2023	Data Augmentationreinforcement-learning	—Unverified	0
GraVIS: Grouping Augmented Views from Independent Sources for Dermatology Analysis	Jan 11, 2023	Contrastive LearningLesion Segmentation	—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