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 201–225 of 10307 papers

TitleStatusHype
A systematic review of challenges and proposed solutions in modeling multimodal data—0
Bridging Ears and Eyes: Analyzing Audio and Visual Large Language Models to Humans in Visible Sound Recognition and Reducing Their Sensory Gap via Cross-Modal Distillation—0
Mixer-Informer-Based Two-Stage Transfer Learning for Long-Sequence Load Forecasting in Newly Constructed Electric Vehicle Charging Stations—0
Development of a WAZOBIA-Named Entity Recognition System—0
NSF-MAP: Neurosymbolic Multimodal Fusion for Robust and Interpretable Anomaly Prediction in Assembly PipelinesCode0
Robust & Precise Knowledge Distillation-based Novel Context-Aware Predictor for Disease Detection in Brain and Gastrointestinal—0
Multimodal Integrated Knowledge Transfer to Large Language Models through Preference Optimization with Biomedical ApplicationsCode0
Automated Knot Detection and Pairing for Wood Analysis in the Timber Industry—0
HyperspectralMAE: The Hyperspectral Imagery Classification Model using Fourier-Encoded Dual-Branch Masked Autoencoder—0
Leveraging Multi-Task Learning for Multi-Label Power System Security Assessment—0
The Application of Deep Learning for Lymph Node Segmentation: A Systematic Review—0
VaCDA: Variational Contrastive Alignment-based Scalable Human Activity Recognition—0
Prompted Meta-Learning for Few-shot Knowledge Graph Completion—0
Improved Brain Tumor Detection in MRI: Fuzzy Sigmoid Convolution in Deep Learning—0
Structural Alignment in Link PredictionCode0
Componential Prompt-Knowledge Alignment for Domain Incremental LearningCode1
Sustainable Smart Farm Networks: Enhancing Resilience and Efficiency with Decision Theory-Guided Deep Reinforcement Learning—0
HMAE: Self-Supervised Few-Shot Learning for Quantum Spin Systems—0
Multi-modal cascade feature transfer for polymer property prediction—0
Comparative Analysis of Lightweight Deep Learning Models for Memory-Constrained Devices—0
RAIL: Region-Aware Instructive Learning for Semi-Supervised Tooth Segmentation in CBCTCode1
Advanced Clustering Framework for Semiconductor Image Analytics Integrating Deep TDA with Self-Supervised and Transfer Learning Techniques—0
Aerodynamic and structural airfoil shape optimisation via Transfer Learning-enhanced Deep Reinforcement Learning—0
Finger Pose Estimation for Under-screen Fingerprint SensorCode0
Early Prediction of Sepsis: Feature-Aligned Transfer Learning—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