SOTAVerified

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 AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2651–2675 of 10307 papers

TitleStatusHype
Gated Domain Units for Multi-source Domain GeneralizationCode0
FedPCL-CDR: A Federated Prototype-based Contrastive Learning Framework for Privacy-Preserving Cross-domain RecommendationCode0
Federated Machine Learning: Concept and ApplicationsCode0
Is One Teacher Model Enough to Transfer Knowledge to a Student Model?Code0
Federated Semi-Supervised Multi-Task Learning to Detect COVID-19 and Lungs Segmentation Marking Using Chest Radiography Images and Raspberry Pi Devices: An Internet of Medical Things ApplicationCode0
Federated Domain Generalization via Prompt Learning and AggregationCode0
Federated Continual Graph LearningCode0
Deep Learning based Intelligent Coin-tap Test for Defect RecognitionCode0
Crosslingual Transfer Learning for Low-Resource Languages Based on Multilingual Colexification GraphsCode0
Fed-CO2: Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federated LearningCode0
Federated Continual Learning for Text Classification via Selective Inter-client TransferCode0
KartalOl: Transfer learning using deep neural network for iris segmentation and localization: New dataset for iris segmentationCode0
Multi-modal Speech Emotion Recognition via Feature Distribution Adaptation NetworkCode0
Deep learning-based spatio-temporal fusion for high-fidelity ultra-high-speed x-ray radiographyCode0
Feature-Based Transfer Learning for Network SecurityCode0
Cross-lingual Transfer Learning for Fake News Detector in a Low-Resource LanguageCode0
A Comparative Analysis of Machine Learning Approaches for Automated Face Mask Detection During COVID-19Code0
FBDNN: Filter Banks and Deep Neural Networks for Portable and Fast Brain-Computer InterfacesCode0
Attention Based Fully Convolutional Network for Speech Emotion RecognitionCode0
FBK-DH at SemEval-2020 Task 12: Using Multi-channel BERT for Multilingual Offensive Language DetectionCode0
AlphaX: eXploring Neural Architectures with Deep Neural Networks and Monte Carlo Tree SearchCode0
Fast Solar Image Classification Using Deep Learning and its Importance for Automation in Solar PhysicsCode0
A comparison of small sample methods for Handshape RecognitionCode0
An Embarrassingly Simple Approach for Knowledge DistillationCode0
Fast Enhanced CT Metal Artifact Reduction using Data Domain Deep LearningCode0
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All datasetsOffice-Home100 sleep nights of 8 caregiversBanglaLekha Isolated DatasetCOCO70KITTI Object Tracking Evaluation 2012Retinal Fundus MultiDisease Image Dataset (RFMiD)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1APCLIPAccuracy84.2—Unverified
2DFA-ENTAccuracy69.2—Unverified
3DFA-SAFNAccuracy69.1—Unverified
4EasyTLAccuracy63.3—Unverified
5MEDAAccuracy60.3—Unverified
#ModelMetricClaimedVerifiedStatus
1CNN10-20% Mask PSNR3.23—Unverified
#ModelMetricClaimedVerifiedStatus
1Chatterjee, Dutta et al.[1]Accuracy96.12—Unverified
#ModelMetricClaimedVerifiedStatus
1Co-TuningAccuracy85.65—Unverified
#ModelMetricClaimedVerifiedStatus
1Physical AccessEER5.74—Unverified
#ModelMetricClaimedVerifiedStatus
1riadd.aucmediAUROC0.95—Unverified