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

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Showing 1921–1930 of 10307 papers

Title	Date	Tasks	Status	Score
Hierarchical transfer learning with applications for electricity load forecasting	Nov 16, 2021	Additive modelsLoad Forecasting	CodeCode Available	5
Hacking Task Confounder in Meta-Learning	Dec 10, 2023	Meta-LearningTransfer Learning	CodeCode Available	5
GYM at Qur’an QA 2023 Shared Task: Multi-Task Transfer Learning for Quranic Passage Retrieval and Question Answering with Large Language Models	Dec 7, 2023	Multi-Task LearningPassage Retrieval	CodeCode Available	5
HACS: Human Action Clips and Segments Dataset for Recognition and Temporal Localization	Dec 26, 2017	Action ClassificationAction Localization	CodeCode Available	5
Combined Model for Partially-Observable and Non-Observable Task Switching: Solving Hierarchical Reinforcement Learning Problems Statically and Dynamically with Transfer Learning	Nov 23, 2019	Hierarchical Reinforcement LearningReinforcement Learning	CodeCode Available	5
Combined Model for Partially-Observable and Non-Observable Task Switching: Solving Hierarchical Reinforcement Learning Problems Statically and Dynamically with Transfer Learning	Apr 13, 2020	Hierarchical Reinforcement LearningReinforcement Learning	CodeCode Available	5
Guided Transfer Learning	Mar 26, 2023	Transfer Learning	CodeCode Available	5
An Optimization Framework for Processing and Transfer Learning for the Brain Tumor Segmentation	Feb 10, 2024	Brain Tumor SegmentationImage Segmentation	CodeCode Available	5
GVdoc: Graph-based Visual Document Classification	May 26, 2023	ClassificationDocument Classification	CodeCode Available	5
GTNet: Generative Transfer Network for Zero-Shot Object Detection	Jan 19, 2020	Generative Adversarial NetworkObject	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