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 4101–4110 of 10307 papers

Title	Date	Tasks	Status
(DE)^2 CO: Deep Depth Colorization	Mar 31, 2017	ColorizationTransfer Learning	—Unverified
Automated Knot Detection and Pairing for Wood Analysis in the Timber Industry	May 9, 2025	Transfer LearningTriplet	—Unverified
DDTCDR: Deep Dual Transfer Cross Domain Recommendation	Oct 11, 2019	Recommendation SystemsTransfer Learning	—Unverified
Automated Identification of Tree Species by Bark Texture Classification Using Convolutional Neural Networks	Oct 3, 2022	Texture ClassificationTransfer Learning	—Unverified
A Multi-input Multi-output Transformer-based Hybrid Neural Network for Multi-class Privacy Disclosure Detection	Aug 19, 2021	Transfer Learning	—Unverified
Generating Realistic COVID19 X-rays with a Mean Teacher + Transfer Learning GAN	Sep 26, 2020	image-classificationImage Classification	—Unverified
Generating Synthetic Datasets for Few-shot Prompt Tuning	Oct 8, 2024	Few-Shot LearningMRPC	—Unverified
DCNNs: A Transfer Learning comparison of Full Weapon Family threat detection for Dual-Energy X-Ray Baggage Imagery	Jun 23, 2020	ClassificationGeneral Classification	—Unverified
DATSING: Data Augmented Time Series Forecasting with Adversarial Domain Adaptation	Oct 19, 2020	Domain AdaptationTime Series	—Unverified
Automated identification of neural cells in the multi-photon images using deep-neural networks	Sep 25, 2019	ClassificationGeneral Classification	—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