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 2801–2825 of 10307 papers

Title	Date	Tasks	Status	Score
Deep Transfer Learning for Multiple Class Novelty Detection	Mar 6, 2019	Novelty DetectionTransfer Learning	CodeCode Available	5
Deep Transfer Learning for Multi-source Entity Linkage via Domain Adaptation	Oct 27, 2021	AttributeDomain Adaptation	CodeCode Available	5
Facial Landmark Predictions with Applications to Metaverse	Sep 29, 2022	Decodertext-to-speech	CodeCode Available	5
Facial Emotion Recognition Under Mask Coverage Using a Data Augmentation Technique	Dec 3, 2023	Data AugmentationDecision Making	CodeCode Available	5
Facilitating the sharing of electrophysiology data analysis results through in-depth provenance capture	Nov 16, 2023	Transfer Learning	CodeCode Available	5
fairseq S2T: Fast Speech-to-Text Modeling with fairseq	Oct 11, 2020	Machine TranslationMulti-Task Learning	CodeCode Available	5
Fleet Control using Coregionalized Gaussian Process Policy Iteration	Nov 22, 2019	Gaussian Processesreinforcement-learning	CodeCode Available	5
Manipulating Transfer Learning for Property Inference	Mar 21, 2023	Transfer Learning	CodeCode Available	5
GLoMo: Unsupervisedly Learned Relational Graphs as Transferable Representations	Jun 14, 2018	image-classificationImage Classification	CodeCode Available	5
Improving Response Time of Home IoT Services in Federated Learning	Feb 28, 2022	Distributed ComputingFederated Learning	CodeCode Available	5
Exploring User Retrieval Integration towards Large Language Models for Cross-Domain Sequential Recommendation	Jun 5, 2024	Contrastive LearningLanguage Modelling	CodeCode Available	5
Bayesian Multi-Task Transfer Learning for Soft Prompt Tuning	Feb 13, 2024	Transfer Learning	CodeCode Available	5
A Little Annotation does a Lot of Good: A Study in Bootstrapping Low-resource Named Entity Recognizers	Aug 23, 2019	Active LearningCross-Lingual Transfer	CodeCode Available	5
Uncovering the Handwritten Text in the Margins: End-to-end Handwritten Text Detection and Recognition	Mar 10, 2023	Data AugmentationHandwritten Text Recognition	CodeCode Available	5
Cross-domain Transfer Learning and State Inference for Soft Robots via a Semi-supervised Sequential Variational Bayes Framework	Mar 3, 2023	Transfer Learning	CodeCode Available	5
Extending LLMs to New Languages: A Case Study of Llama and Persian Adaptation	Dec 17, 2024	Classificationparameter-efficient fine-tuning	CodeCode Available	5
Alioth: A Machine Learning Based Interference-Aware Performance Monitor for Multi-Tenancy Applications in Public Cloud	Jul 18, 2023	CPUDecision Making	CodeCode Available	5
Transformers on Multilingual Clause-Level Morphology	Nov 3, 2022	Data AugmentationLanguage Modelling	CodeCode Available	5
Speech foundation models in healthcare: Effect of layer selection on pathological speech feature prediction	Feb 2, 2024	Transfer Learning	CodeCode Available	5
Cross-Domain Self-supervised Multi-task Feature Learning using Synthetic Imagery	Nov 24, 2017	Domain AdaptationMulti-Task Learning	CodeCode Available	5
Exploring the Limits of Weakly Supervised Pretraining	May 2, 2018	General Classificationimage-classification	CodeCode Available	5
Exploring the potential of transfer learning for metamodels of heterogeneous material deformation	Oct 28, 2020	CPUTransfer Learning	CodeCode Available	5
Exploring the Benefits of Differentially Private Pre-training and Parameter-Efficient Fine-tuning for Table Transformers	Sep 12, 2023	parameter-efficient fine-tuningTransfer Learning	CodeCode Available	5
3DLaneNAS: Neural Architecture Search for Accurate and Light-Weight 3D Lane Detection	Sep 6, 2022	3D Lane DetectionAutonomous Driving	CodeCode Available	5
Exploring the Benefits of Visual Prompting in Differential Privacy	Mar 22, 2023	image-classificationImage Classification	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