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 3271–3280 of 10307 papers

Title	Date	Tasks	Status	Score
Contrastive learning of T cell receptor representations	Jun 10, 2024	Contrastive LearningLanguage Modeling	CodeCode Available	5
CL-MRI: Self-Supervised Contrastive Learning to Improve the Accuracy of Undersampled MRI Reconstruction	Jun 1, 2023	AnatomyContrastive Learning	CodeCode Available	5
Equivariant Learning of Stochastic Fields: Gaussian Processes and Steerable Conditional Neural Processes	Nov 25, 2020	Gaussian ProcessesTransfer Learning	CodeCode Available	5
Environment Invariant Linear Least Squares	Mar 6, 2023	Causal Inferenceregression	CodeCode Available	5
An Open-set Recognition and Few-Shot Learning Dataset for Audio Event Classification in Domestic Environments	Feb 26, 2020	Face RecognitionFew-Shot Learning	CodeCode Available	5
Dynamic Guidance Adversarial Distillation with Enhanced Teacher Knowledge	Sep 3, 2024	Transfer Learning	CodeCode Available	5
Semi-Supervised Learning with Scarce Annotations	May 21, 2019	Multi-class ClassificationSelf-Supervised Learning	CodeCode Available	5
EPRNet: Efficient Pyramid Representation Network for Real-Time Street Scene Segmentation	Mar 23, 2021	image-classificationImage Classification	CodeCode Available	5
Estimating encoding models of cortical auditory processing using naturalistic stimuli and transfer learning	Sep 11, 2019	Transfer Learning	CodeCode Available	5
Ensemble Modeling with Contrastive Knowledge Distillation for Sequential Recommendation	Apr 28, 2023	AttributeContrastive Learning	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