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 821–830 of 10307 papers

Title	Date	Tasks	Status	Hype
Enhanced Gaussian Process Dynamical Models with Knowledge Transfer for Long-term Battery Degradation Forecasting	Dec 3, 2022	Transfer Learning	CodeCode Available	1
How Hateful are Movies? A Study and Prediction on Movie Subtitles	Aug 19, 2021	Domain AdaptationTransfer Learning	CodeCode Available	1
A Closer Look at the Few-Shot Adaptation of Large Vision-Language Models	Dec 20, 2023	Transfer Learning	CodeCode Available	1
HSVA: Hierarchical Semantic-Visual Adaptation for Zero-Shot Learning	Sep 30, 2021	Transfer LearningZero-Shot Learning	CodeCode Available	1
Conditional Generative Models for Simulation of EMG During Naturalistic Movements	Nov 3, 2022	Data AugmentationTransfer Learning	CodeCode Available	1
Hybrid thermal modeling of additive manufacturing processes using physics-informed neural networks for temperature prediction and parameter identification	Jun 15, 2022	Transfer Learning	CodeCode Available	1
Hyper-Representations as Generative Models: Sampling Unseen Neural Network Weights	Sep 29, 2022	Knowledge DistillationNeural Architecture Search	CodeCode Available	1
Hyper-Representations for Pre-Training and Transfer Learning	Jul 22, 2022	Knowledge DistillationNeural Architecture Search	CodeCode Available	1
Hyperspectral Classification Based on Lightweight 3-D-CNN With Transfer Learning	Dec 7, 2020	ClassificationGeneral Classification	CodeCode Available	1
Bayesian Optimization with Automatic Prior Selection for Data-Efficient Direct Policy Search	Sep 20, 2017	Bayesian OptimizationTransfer 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