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 1741–1750 of 10307 papers

Title	Date	Tasks	Status
Active Learning Approaches to Enhancing Neural Machine Translation	Nov 1, 2020	Active LearningMachine Translation	—Unverified
Context-Aware Policy Reuse	Jun 11, 2018	Reinforcement LearningTransfer Learning	—Unverified
Active flow control for three-dimensional cylinders through deep reinforcement learning	Sep 4, 2023	Deep Reinforcement LearningMulti-agent Reinforcement Learning	—Unverified
Adversarial-Robust Transfer Learning for Medical Imaging via Domain Assimilation	Feb 25, 2024	DiagnosticMedical Diagnosis	—Unverified
Content-Based Brain Tumor Retrieval for MR Images Using Transfer Learning	Jan 4, 2019	Content-Based Image RetrievalImage Retrieval	—Unverified
Application of Transfer Learning Approaches in Multimodal Wearable Human Activity Recognition	Jul 8, 2017	Activity RecognitionEnsemble Learning	—Unverified
Application of Transfer Learning and Ensemble Learning in Image-level Classification for Breast Histopathology	Apr 18, 2022	Binary ClassificationClassification	—Unverified
Adversarial Robustness of Discriminative Self-Supervised Learning in Vision	Mar 8, 2025	Adversarial RobustnessLinear evaluation	—Unverified
Adversarial Network Compression	Mar 28, 2018	Neural Network CompressionTransfer Learning	—Unverified
Application of Low-resource Machine Translation Techniques to Russian-Tatar Language Pair	Oct 1, 2019	Machine TranslationTransfer Learning	—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