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 4461–4470 of 10307 papers

Title	Date	Tasks	Status	Hype
Fine-Tuning BERT for Automatic ADME Semantic Labeling in FDA Drug Labeling to Enhance Product-Specific Guidance Assessment	Jul 25, 2022	Transfer Learning	—Unverified	0
W2N:Switching From Weak Supervision to Noisy Supervision for Object Detection	Jul 25, 2022	Objectobject-detection	CodeCode Available	1
From Multi-label Learning to Cross-Domain Transfer: A Model-Agnostic Approach	Jul 24, 2022	Multi-Label LearningMulti-Task Learning	—Unverified	0
ArmanEmo: A Persian Dataset for Text-based Emotion Detection	Jul 24, 2022	Emotion ClassificationTransfer Learning	CodeCode Available	0
Online Knowledge Distillation via Mutual Contrastive Learning for Visual Recognition	Jul 23, 2022	Contrastive Learningimage-classification	CodeCode Available	1
Self-supervised contrastive learning of echocardiogram videos enables label-efficient cardiac disease diagnosis	Jul 23, 2022	ClassificationContrastive Learning	CodeCode Available	1
Hyper-Representations for Pre-Training and Transfer Learning	Jul 22, 2022	Knowledge DistillationNeural Architecture Search	CodeCode Available	1
Multi-Level Fine-Tuning, Data Augmentation, and Few-Shot Learning for Specialized Cyber Threat Intelligence	Jul 22, 2022	Data AugmentationFew-Shot Learning	—Unverified	0
AdaptCL: Adaptive Continual Learning for Tackling Heterogeneity in Sequential Datasets	Jul 22, 2022	Continual LearningTransfer Learning	CodeCode Available	0
Deep transfer learning method based on automatic domain alignment and moment matching	Jul 21, 2022	Domain AdaptationImage Classification	CodeCode Available	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