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

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Showing 2626–2650 of 10307 papers

TitleStatusHype
Automatic Machine Learning Framework to Study Morphological Parameters of AGN Host Galaxies within z < 1.4 in the Hyper Supreme-Cam Wide SurveyCode0
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
FedRef: Communication-Efficient Bayesian Fine Tuning with Reference ModelCode0
Few-shot classification in Named Entity Recognition TaskCode0
Deep into The Domain Shift: Transfer Learning through Dependence RegularizationCode0
A Generative Framework for Low-Cost Result Validation of Machine Learning-as-a-Service InferenceCode0
Deep Learning Algorithms for Hedging with FrictionsCode0
Fuzzy Rank-based Fusion of CNN Models using Gompertz Function for Screening COVID-19 CT-ScansCode0
Federated Continual Graph LearningCode0
Analysing Cross-Lingual Transfer in Low-Resourced African Named Entity RecognitionCode0
Integrated Parameter-Efficient Tuning for General-Purpose Audio ModelsCode0
Integrating Transformer and Autoencoder Techniques with Spectral Graph Algorithms for the Prediction of Scarcely Labeled Molecular DataCode0
Deep Learning and Transfer Learning Architectures for English Premier League Player Performance ForecastingCode0
Interpretable Embedding Procedure Knowledge Transfer via Stacked Principal Component Analysis and Graph Neural NetworkCode0
Interpretable neural architecture search and transfer learning for understanding CRISPR/Cas9 off-target enzymatic reactionsCode0
Deep learning approaches in food recognitionCode0
Interpretations of Domain Adaptations via Layer Variational AnalysisCode0
Federated Continual Learning for Text Classification via Selective Inter-client TransferCode0
Fed-CO2: Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federated LearningCode0
Deep learning approach to Fourier ptychographic microscopyCode0
Multi-modal Speech Emotion Recognition via Feature Distribution Adaptation NetworkCode0
Crosslingual Transfer Learning for Low-Resource Languages Based on Multilingual Colexification GraphsCode0
Feature-Based Transfer Learning for Network SecurityCode0
Bayesian Inverse Transfer in Evolutionary Multiobjective OptimizationCode0
An Embarrassingly Simple Approach for Knowledge DistillationCode0
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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