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 2631–2640 of 10307 papers

Title	Date	Tasks	Status	Score
Functional Knowledge Transfer with Self-supervised Representation Learning	Mar 12, 2023	Representation LearningSelf-Supervised Learning	CodeCode Available	5
Deep Learning Algorithms for Hedging with Frictions	Nov 2, 2021	Deep LearningTime Series Analysis	CodeCode Available	5
An AI-Powered VVPAT Counter for Elections in India	Dec 9, 2022	Few-Shot LearningImage Classification	CodeCode Available	5
Initialization Matters for Adversarial Transfer Learning	Dec 10, 2023	Adversarial Robustnessimage-classification	CodeCode Available	5
Analysing Cross-Lingual Transfer in Low-Resourced African Named Entity Recognition	Sep 11, 2023	Cross-Lingual Transfernamed-entity-recognition	CodeCode Available	5
An Embarrassingly Simple Approach for Knowledge Distillation	Dec 5, 2018	Face RecognitionKnowledge Distillation	CodeCode Available	5
Multi-modal Speech Emotion Recognition via Feature Distribution Adaptation Network	Oct 29, 2024	Emotion RecognitionSpeech Emotion Recognition	CodeCode Available	5
Feature-Based Transfer Learning for Network Security	Dec 11, 2017	Transfer Learning	CodeCode Available	5
Integrating Transformer and Autoencoder Techniques with Spectral Graph Algorithms for the Prediction of Scarcely Labeled Molecular Data	Nov 12, 2022	Transfer Learning	CodeCode Available	5
Fed-CO2: Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federated Learning	Dec 21, 2023	Domain GeneralizationFederated 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