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

Showing 23762400 of 10307 papers

TitleStatusHype
Entity Tracking via Effective Use of Multi-Task Learning Model and Mention-guided DecodingCode0
Fuzzy Rank-based Fusion of CNN Models using Gompertz Function for Screening COVID-19 CT-ScansCode0
General-Purpose Deep Point Cloud Feature ExtractorCode0
Graph Constrained Data Representation Learning for Human Motion SegmentationCode0
Knowledge transfer across cell lines using Hybrid Gaussian Process models with entity embedding vectorsCode0
FOSI: Hybrid First and Second Order OptimizationCode0
Foundation Model for Composite Microstructures: Reconstruction, Stiffness, and Nonlinear Behavior PredictionCode0
Forecasting Future Humphrey Visual Fields Using Deep LearningCode0
A Multi-lingual Multi-task Architecture for Low-resource Sequence LabelingCode0
Forecasting new diseases in low-data settings using transfer learningCode0
Automated Long Answer Grading with RiceChem DatasetCode0
Advance Warning Methodologies for COVID-19 using Chest X-Ray ImagesCode0
Food Classification with Convolutional Neural Networks and Multi-Class Linear Discernment AnalysisCode0
Force myography benchmark data for hand gesture recognition and transfer learningCode0
FM-OV3D: Foundation Model-based Cross-modal Knowledge Blending for Open-Vocabulary 3D DetectionCode0
Adaptive Transfer Clustering: A Unified FrameworkCode0
Focus on the Positives: Self-Supervised Learning for Biodiversity MonitoringCode0
Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networksCode0
A Multifactorial Optimization Paradigm for Linkage Tree Genetic AlgorithmCode0
Flexible Option LearningCode0
Flat Posterior Does Matter For Bayesian Model AveragingCode0
Fleet Control using Coregionalized Gaussian Process Policy IterationCode0
FOIT: Fast Online Instance Transfer for Improved EEG Emotion RecognitionCode0
Fracture Detection in Wrist X-ray Images Using Deep Learning-Based Object Detection ModelsCode0
FissionFusion: Fast Geometric Generation and Hierarchical Souping for Medical Image AnalysisCode0
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Benchmark Results

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