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 761–770 of 10307 papers

Title	Date	Tasks	Status	Hype
Federated Transfer Learning for EEG Signal Classification	Apr 26, 2020	ClassificationDomain Adaptation	CodeCode Available	1
FedLPS: Heterogeneous Federated Learning for Multiple Tasks with Local Parameter Sharing	Feb 13, 2024	Edge-computingFederated Learning	CodeCode Available	1
FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated Learning	Dec 14, 2023	Computational EfficiencyFederated Learning	CodeCode Available	1
Few-Sample Named Entity Recognition for Security Vulnerability Reports by Fine-Tuning Pre-Trained Language Models	Aug 14, 2021	named-entity-recognitionNamed Entity Recognition	CodeCode Available	1
Bridge to Target Domain by Prototypical Contrastive Learning and Label Confusion: Re-explore Zero-Shot Learning for Slot Filling	Oct 7, 2021	Contrastive Learningslot-filling	CodeCode Available	1
Few-Shot Keyword Spotting in Any Language	Apr 3, 2021	Keyword SpottingTransfer Learning	CodeCode Available	1
Boosting Memory Efficiency in Transfer Learning for High-Resolution Medical Image Classification	Aug 5, 2024	image-classificationImage Classification	CodeCode Available	1
AP-10K: A Benchmark for Animal Pose Estimation in the Wild	Aug 28, 2021	Animal Pose EstimationDiversity	CodeCode Available	1
Fine-tuning giant neural networks on commodity hardware with automatic pipeline model parallelism	Jul 14, 2021	GPUTransfer Learning	CodeCode Available	1
A Whisper transformer for audio captioning trained with synthetic captions and transfer learning	May 15, 2023	Audio captioningSpeech-to-Text	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