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 4341–4350 of 10307 papers

Title	Date	Tasks	Status	Hype
Transformer-CNN Cohort: Semi-supervised Semantic Segmentation by the Best of Both Students	Sep 6, 2022	Semantic SegmentationSemi-Supervised Semantic Segmentation	—Unverified	0
Transfer Learning of Lexical Semantic Families for Argumentative Discourse Units Identification	Sep 6, 2022	Argument MiningTransfer Learning	—Unverified	0
Topology Change Aware Data-Driven Probabilistic Distribution State Estimation Based on Gaussian Process	Sep 5, 2022	Bayesian InferenceState Estimation	—Unverified	0
Unsupervised Domain Adaptation via Style-Aware Self-intermediate Domain	Sep 5, 2022	Domain AdaptationTransfer Learning	—Unverified	0
Federated Transfer Learning with Multimodal Data	Sep 5, 2022	Federated LearningSelf-Supervised Learning	—Unverified	0
Domain Generalization for Prostate Segmentation in Transrectal Ultrasound Images: A Multi-center Study	Sep 5, 2022	Domain AdaptationDomain Generalization	CodeCode Available	1
A Study on Representation Transfer for Few-Shot Learning	Sep 5, 2022	Classificationfeature selection	—Unverified	0
Generalization in Neural Networks: A Broad Survey	Sep 4, 2022	Domain AdaptationDomain Generalization	—Unverified	0
Class-Specific Channel Attention for Few-Shot Learning	Sep 3, 2022	Few-Shot Classification and SegmentationFew-Shot Image Classification	—Unverified	0
Classification of Breast Tumours Based on Histopathology Images Using Deep Features and Ensemble of Gradient Boosting Methods	Sep 3, 2022	Breast Cancer DetectionTransfer Learning	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