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 1561–1570 of 10307 papers

Title	Date	Tasks	Status	Score
HyperBO+: Pre-training a universal prior for Bayesian optimization with hierarchical Gaussian processes	Dec 20, 2022	Bayesian OptimizationGaussian Processes	CodeCode Available	5
A Gated Self-attention Memory Network for Answer Selection	Sep 13, 2019	Answer SelectionTransfer Learning	CodeCode Available	5
A Semi-supervised Deep Transfer Learning Approach for Rolling-Element Bearing Remaining Useful Life Prediction	Sep 29, 2021	Interpretability Techniques for Deep LearningManagement	CodeCode Available	5
ADA-Net: Attention-Guided Domain Adaptation Network with Contrastive Learning for Standing Dead Tree Segmentation Using Aerial Imagery	Apr 5, 2025	Contrastive LearningDomain Adaptation	CodeCode Available	5
How good are variational autoencoders at transfer learning?	Apr 21, 2023	Medical Image AnalysisMusic Generation	CodeCode Available	5
How Language-Neutral is Multilingual BERT?	Nov 8, 2019	RetrievalSentence	CodeCode Available	5
Hostility Detection in Hindi leveraging Pre-Trained Language Models	Jan 14, 2021	Fake News DetectionHate Speech Detection	CodeCode Available	5
Homogeneous Online Transfer Learning with Online Distribution Discrepancy Minimization	Dec 31, 2019	Transfer Learning	CodeCode Available	5
HOUDINI: Lifelong Learning as Program Synthesis	Mar 31, 2018	Lifelong learningProgram Synthesis	CodeCode Available	5
AGA: Attribute-Guided Augmentation	Jul 1, 2017	AttributeData Augmentation	CodeCode Available	5

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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