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 1811–1820 of 10307 papers

Title	Date	Tasks	Status	Score
Classifying Textual Data with Pre-trained Vision Models through Transfer Learning and Data Transformations	Jun 23, 2021	Domain Adaptationimage-classification	CodeCode Available	5
Hi-gMISnet: generalized medical image segmentation using DWT based multilayer fusion and dual mode attention into high resolution pGAN	May 20, 2024	Generative Adversarial NetworkImage Segmentation	CodeCode Available	5
Hierarchical Topology Isomorphism Expertise Embedded Graph Contrastive Learning	Dec 21, 2023	Contrastive LearningGraph Representation Learning	CodeCode Available	5
Invariant Models for Causal Transfer Learning	Jul 19, 2015	Domain GeneralizationTransfer Learning	CodeCode Available	5
A Systematic Comparison of Architectures for Document-Level Sentiment Classification	Feb 19, 2020	ClassificationDocument Classification	CodeCode Available	5
Investigating Numeracy Learning Ability of a Text-to-Text Transfer Model	Sep 10, 2021	Transfer Learning	CodeCode Available	5
Hierarchical transfer learning with applications for electricity load forecasting	Nov 16, 2021	Additive modelsLoad Forecasting	CodeCode Available	5
Investigating Transferability in Pretrained Language Models	Apr 30, 2020	Language ModelingLanguage Modelling	CodeCode Available	5
Advancing Transformer's Capabilities in Commonsense Reasoning	Oct 10, 2023	Transfer Learning	CodeCode Available	5
Catastrophic Forgetting Meets Negative Transfer: Batch Spectral Shrinkage for Safe Transfer Learning	Dec 1, 2019	Transfer Learning	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