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 2131–2140 of 10307 papers

Title	Date	Tasks	Status	Score
Geographical Distance Is The New Hyperparameter: A Case Study Of Finding The Optimal Pre-trained Language For English-isiZulu Machine Translation	May 17, 2022	Low-Resource Neural Machine TranslationMachine Translation	CodeCode Available	5
Generative Causal Representation Learning for Out-of-Distribution Motion Forecasting	Feb 17, 2023	Motion ForecastingRepresentation Learning	CodeCode Available	5
Generating Thermal Human Faces for Physiological Assessment Using Thermal Sensor Auxiliary Labels	Jun 15, 2021	SSIMTransfer Learning	CodeCode Available	5
Generative Denoise Distillation: Simple Stochastic Noises Induce Efficient Knowledge Transfer for Dense Prediction	Jan 16, 2024	Instance SegmentationKnowledge Distillation	CodeCode Available	5
Bilingual Alignment Pre-Training for Zero-Shot Cross-Lingual Transfer	Jun 3, 2021	Cross-Lingual TransferLanguage Modelling	CodeCode Available	5
Generating Gameplay-Relevant Art Assets with Transfer Learning	Oct 4, 2020	Image GenerationTransfer Learning	CodeCode Available	5
Generalizing over Long Tail Concepts for Medical Term Normalization	Oct 21, 2022	AttributeTransfer Learning	CodeCode Available	5
A Deep Learning Method for Comparing Bayesian Hierarchical Models	Jan 27, 2023	Decision MakingDeep Learning	CodeCode Available	5
Generalizing Teacher Networks for Effective Knowledge Distillation Across Student Architectures	Jul 22, 2024	Knowledge DistillationModel Compression	CodeCode Available	5
Bilateral Personalized Dialogue Generation with Contrastive Learning	Jun 15, 2021	Contrastive LearningDialogue Generation	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