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 4711–4720 of 10307 papers

Title	Date	Tasks	Status
Learning Implicit Generative Models by Matching Perceptual Features	Apr 4, 2019	Style TransferSuper-Resolution	—Unverified
A study on the plasticity of neural networks	May 31, 2021	Continual LearningTransfer Learning	—Unverified
Learning Interpretable Hierarchical Dynamical Systems Models from Time Series Data	Oct 7, 2024	Time SeriesTransfer Learning	—Unverified
Learning Invariant Feature Spaces to Transfer Skills with Reinforcement Learning	Mar 8, 2017	reinforcement-learningReinforcement Learning	—Unverified
Learning Invariant Representations across Domains and Tasks	Mar 3, 2021	Domain Adaptationimage-classification	—Unverified
Learning Invariant Representations for Sentiment Analysis: The Missing Material is Datasets	Jul 29, 2019	Domain AdaptationSentiment Analysis	—Unverified
Learning Losses for Strategic Classification	Mar 25, 2022	ClassificationLearning Theory	—Unverified
Learning Modality-Invariant Representations for Speech and Images	Dec 11, 2017	Information RetrievalRetrieval	—Unverified
Learning Modular Neural Network Policies for Multi-Task and Multi-Robot Transfer	Sep 22, 2016	Deep Reinforcement Learningreinforcement-learning	—Unverified
Learning More Generalized Experts by Merging Experts in Mixture-of-Experts	May 19, 2024	Incremental LearningMixture-of-Experts	—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