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 1691–1700 of 10307 papers

Title	Date	Tasks	Status	Score
How Well Do Vision Transformers (VTs) Transfer To The Non-Natural Image Domain? An Empirical Study Involving Art Classification	Aug 9, 2022	Transfer Learning	CodeCode Available	5
Application of Transfer Learning to Sign Language Recognition using an Inflated 3D Deep Convolutional Neural Network	Feb 25, 2021	Deep LearningSign Language Recognition	CodeCode Available	5
Transfer Learning for Risk Classification of Social Media Posts: Model Evaluation Study	Jul 4, 2019	AutoMLBIG-bench Machine Learning	CodeCode Available	5
Hostility Detection in Hindi leveraging Pre-Trained Language Models	Jan 14, 2021	Fake News DetectionHate Speech Detection	CodeCode Available	5
HOLMES: HOLonym-MEronym based Semantic inspection for Convolutional Image Classifiers	Mar 13, 2024	image-classificationImage Classification	CodeCode Available	5
Homogeneous Online Transfer Learning with Online Distribution Discrepancy Minimization	Dec 31, 2019	Transfer Learning	CodeCode Available	5
HOUDINI: Lifelong Learning as Program Synthesis	Mar 31, 2018	Lifelong learningProgram Synthesis	CodeCode Available	5
Application of Neural Ordinary Differential Equations for ITER Burning Plasma Dynamics	Aug 26, 2024	Transfer Learning	CodeCode Available	5
Historical Document Image Segmentation with LDA-Initialized Deep Neural Networks	Oct 19, 2017	Image SegmentationSemantic Segmentation	CodeCode Available	5
HIT: A Hierarchically Fused Deep Attention Network for Robust Code-mixed Language Representation	May 30, 2021	Deep AttentionRepresentation Learning	CodeCode Available	5

Show:10 25 50

← PrevPage 170 of 1031Next →

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