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 2221–2230 of 10307 papers

Title	Date	Tasks	Status	Score
General-Purpose Deep Point Cloud Feature Extractor	Mar 12, 2018	3D Object Classification3D Point Cloud Classification	CodeCode Available	5
GenTL: A General Transfer Learning Model for Building Thermal Dynamics	Jan 23, 2025	Deep LearningTransfer Learning	CodeCode Available	5
Deep State Inference: Toward Behavioral Model Inference of Black-box Software Systems	Jan 13, 2021	Anomaly DetectionChange Point Detection	CodeCode Available	5
Context selectivity with dynamic availability enables lifelong continual learning	Jun 2, 2023	Continual LearningLifelong learning	CodeCode Available	5
Gated Domain Units for Multi-source Domain Generalization	Jun 24, 2022	Domain GeneralizationTransfer Learning	CodeCode Available	5
GAN Cocktail: mixing GANs without dataset access	Jun 7, 2021	Transfer Learning	CodeCode Available	5
An Efficient Confidence Measure-Based Evaluation Metric for Breast Cancer Screening Using Bayesian Neural Networks	Aug 12, 2020	image-classificationImage Classification	CodeCode Available	5
GAN pretraining for deep convolutional autoencoders applied to Software-based Fingerprint Presentation Attack Detection	May 21, 2021	Transfer Learning	CodeCode Available	5
An Efficient and Explainable Transformer-Based Few-Shot Learning for Modeling Electricity Consumption Profiles Across Thousands of Domains	Aug 15, 2024	Few-Shot LearningTransfer Learning	CodeCode Available	5
Gammatonegram Representation for End-to-End Dysarthric Speech Processing Tasks: Speech Recognition, Speaker Identification, and Intelligibility Assessment	Jul 6, 2023	Speaker Identificationspeech-recognition	CodeCode Available	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