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 7476–7500 of 10307 papers

TitleStatusHype
Transfer Learning for Bayesian Optimization on Heterogeneous Search Spaces—0
Transfer Learning for Bayesian Optimization: A Survey—0
Transfer Learning for Brain Tumor Segmentation—0
Transfer Learning for British Sign Language Modelling—0
Transfer learning for cancer diagnosis in histopathological images—0
Transfer Learning for Classification of Alzheimer's Disease Based on Genome Wide Data—0
Transfer Learning for Clinical Time Series Analysis using Recurrent Neural Networks—0
Transfer Learning for Clinical Time Series Analysis using Deep Neural Networks—0
Transfer Learning for Constituency-Based Grammars—0
Transfer Learning for Content-Based Recommender Systems using Tree Matching—0
Transfer Learning for Context-Aware Question Matching in Information-seeking Conversations in E-commerce—0
Transfer Learning for Context-Aware Spoken Language Understanding—0
Transfer Learning for Contextual Multi-armed Bandits—0
Transfer Learning for Control Systems via Neural Simulation Relations—0
Transfer Learning for Covert Speech Classification Using EEG Hilbert Envelope and Temporal Fine Structure—0
Transfer Learning for Credit Card Fraud Detection: A Journey from Research to Production—0
Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective—0
Transfer learning for cross-modal demand prediction of bike-share and public transit—0
Transfer Learning for CSI-based Positioning with Multi-environment Meta-learning—0
Transfer Learning for Czech Historical Named Entity Recognition—0
Transfer Learning for Deep-Unfolded Combinatorial Optimization Solver with Quantum Annealer—0
Transfer learning for diagnosis of congenital abnormalities of the kidney and urinary tract in children based on Ultrasound imaging data—0
Transfer Learning for Diffusion Models—0
Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation—0
Transfer Learning for E-commerce Query Product Type Prediction—0
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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