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 9551–9575 of 10307 papers

TitleStatusHype
COVID-Net CXR-S: Deep Convolutional Neural Network for Severity Assessment of COVID-19 Cases from Chest X-ray Images—0
Beyond Fine-tuning: Classifying High Resolution Mammograms using Function-Preserving Transformations—0
Beyond Fine Tuning: A Modular Approach to Learning on Small Data—0
Beyond Efficiency: Molecular Data Pruning for Enhanced Generalization—0
C-Procgen: Empowering Procgen with Controllable Contexts—0
Crack Detection in Infrastructure Using Transfer Learning, Spatial Attention, and Genetic Algorithm Optimization—0
Crackle Detection In Lung Sounds Using Transfer Learning And Multi-Input Convolitional Neural Networks—0
Crack-Net: Prediction of Crack Propagation in Composites—0
BEV-Seg: Bird's Eye View Semantic Segmentation Using Geometry and Semantic Point Cloud—0
Cramnet: Layer-wise Deep Neural Network Compression with Knowledge Transfer from a Teacher Network—0
CReaM: Condensed Real-time Models for Depth Prediction using Convolutional Neural Networks—0
Creation of Novel Soft Robot Designs using Generative AI—0
Membership Privacy for Machine Learning Models Through Knowledge Transfer—0
Between-Domain Instance Transition Via the Process of Gibbs Sampling in RBM—0
Reconnaissance de phones fond\'ee sur du Transfer Learning pour des enfants apprenants lecteurs en environnement de classe (Transfer Learning based phone recognition on children learning to read, with speech recorded in a classroom environment)—0
Credit Risk Meets Large Language Models: Building a Risk Indicator from Loan Descriptions in P2P Lending—0
Reconstructing Human Mobility Pattern: A Semi-Supervised Approach for Cross-Dataset Transfer Learning—0
Better Transfer Learning with Inferred Successor Maps—0
Reconstructing Training Data From Real World Models Trained with Transfer Learning—0
Critical Assessment of Transfer Learning for Medical Image Segmentation with Fully Convolutional Neural Networks—0
Better and Faster: Knowledge Transfer from Multiple Self-supervised Learning Tasks via Graph Distillation for Video Classification—0
CRL: Class Representative Learning for Image Classification—0
CRNNTL: convolutional recurrent neural network and transfer learning for QSAR modelling—0
CromSS: Cross-modal pre-training with noisy labels for remote sensing image segmentation—0
Cronus: Robust and Heterogeneous Collaborative Learning with Black-Box Knowledge Transfer—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