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 331–340 of 10307 papers

Title	Date	Tasks	Status	Hype	Score
A 1D CNN for high accuracy classification and transfer learning in motor imagery EEG-based brain-computer interface	Jan 6, 2022	Brain Computer InterfaceData Augmentation	CodeCode Available	1	5
CLiMB: A Continual Learning Benchmark for Vision-and-Language Tasks	Jun 18, 2022	Continual LearningTransfer Learning	CodeCode Available	1	5
CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noise	Nov 20, 2017	ClassificationGeneral Classification	CodeCode Available	1	5
A Data-Efficient Pan-Tumor Foundation Model for Oncology CT Interpretation	Feb 10, 2025	Lesion SegmentationStructured Report Generation	CodeCode Available	1	5
An Empirical Study on Cross-X Transfer for Legal Judgment Prediction	Sep 25, 2022	Cross-Lingual TransferTransfer Learning	CodeCode Available	1	5
An Empirical Investigation of Model-to-Model Distribution Shifts in Trained Convolutional Filters	Jan 20, 2022	modelTransfer Learning	CodeCode Available	1	5
A Competition Winning Deep Reinforcement Learning Agent in microRTS	Feb 12, 2024	Deep Reinforcement LearningImitation Learning	CodeCode Available	1	5
An Empirical Study on Large-Scale Multi-Label Text Classification Including Few and Zero-Shot Labels	Oct 4, 2020	Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATION	CodeCode Available	1	5
An Ensemble Approach for Automated Theorem Proving Based on Efficient Name Invariant Graph Neural Representations	May 15, 2023	Automated Theorem ProvingTransfer Learning	CodeCode Available	1	5
Choquet Integral and Coalition Game-based Ensemble of Deep Learning Models for COVID-19 Screening from Chest X-ray Images	Sep 9, 2021	COVID-19 DiagnosisEnsemble Learning	CodeCode Available	1	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