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 451–460 of 10307 papers

Title	Date	Tasks	Status	Hype
Development and bilingual evaluation of Japanese medical large language model within reasonably low computational resources	Sep 18, 2024	GPULanguage Modeling	CodeCode Available	1
Reasoning Visual Dialog with Sparse Graph Learning and Knowledge Transfer	Apr 14, 2020	Graph LearningGraph structure learning	CodeCode Available	1
Open-Pose 3D Zero-Shot Learning: Benchmark and Challenges	Dec 12, 2023	3D Object ClassificationClassification	CodeCode Available	1
Diffusion-Based Neural Network Weights Generation	Feb 28, 2024	Meta-LearningRepresentation Learning	CodeCode Available	1
Automated Cloud Provisioning on AWS using Deep Reinforcement Learning	Sep 13, 2017	Cloud ComputingDeep Reinforcement Learning	CodeCode Available	1
AI4COVID-19: AI Enabled Preliminary Diagnosis for COVID-19 from Cough Samples via an App	Apr 2, 2020	COVID-19 DiagnosisTransfer Learning	CodeCode Available	1
Discriminative Feature Alignment: Improving Transferability of Unsupervised Domain Adaptation by Gaussian-guided Latent Alignment	Jun 23, 2020	Data AugmentationDecoder	CodeCode Available	1
Disentangled Knowledge Transfer for OOD Intent Discovery with Unified Contrastive Learning	May 1, 2022	ClusteringContrastive Learning	CodeCode Available	1
Distance-Based Regularisation of Deep Networks for Fine-Tuning	Feb 19, 2020	Transfer Learning	CodeCode Available	1
Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement Learning	Feb 13, 2023	Deep Reinforcement Learningreinforcement-learning	CodeCode Available	1

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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