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 4051–4060 of 10307 papers

Title	Date	Tasks	Status
Few-Shot Nested Named Entity Recognition	Dec 2, 2022	Contrastive LearningFew-Shot Learning	—Unverified
A multilingual training strategy for low resource Text to Speech	Sep 2, 2024	Cross-Lingual Transfertext-to-speech	—Unverified
Few-Shot Object Detection via Knowledge Transfer	Aug 28, 2020	Few-Shot Object DetectionObject	—Unverified
Few-Shot Object Detection with Sparse Context Transformers	Feb 14, 2024	Few-Shot Object DetectionObject	—Unverified
Gender bias Evaluation in Luganda-English Machine Translation	Sep 1, 2022	Embeddings EvaluationFairness	—Unverified
A Close Look at Few-shot Real Image Super-resolution from the Distortion Relation Perspective	Nov 25, 2021	Image RestorationImage Super-Resolution	—Unverified
Deciphering and Optimizing Multi-Task Learning: a Random Matrix Approach	Jan 1, 2021	Multi-Task LearningTransfer Learning	—Unverified
Deception Detection with Feature-Augmentation by soft Domain Transfer	May 1, 2023	Deception DetectionTransfer Learning	—Unverified
Automated PII Extraction from Social Media for Raising Privacy Awareness: A Deep Transfer Learning Approach	Nov 11, 2021	Embeddings EvaluationFeature Engineering	—Unverified
Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions	Jul 5, 2020	Decision Makingreinforcement-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