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 976–1000 of 10307 papers

TitleStatusHype
A Hybrid Approach for COVID-19 Detection: Combining Wasserstein GAN with Transfer Learning—0
Deep Nonparametric Conditional Independence Tests for ImagesCode0
Cross-Domain Transfer Learning using Attention Latent Features for Multi-Agent Trajectory Prediction—0
Towards Equitable ASD Diagnostics: A Comparative Study of Machine and Deep Learning Models Using Behavioral and Facial Data—0
Predicting Stroke through Retinal Graphs and Multimodal Self-supervised LearningCode0
AGE2HIE: Transfer Learning from Brain Age to Predicting Neurocognitive Outcome for Infant Brain Injury—0
Enhancing Bronchoscopy Depth Estimation through Synthetic-to-Real Domain Adaptation—0
wav2sleep: A Unified Multi-Modal Approach to Sleep Stage Classification from Physiological SignalsCode1
Fine-tuning -- a Transfer Learning approach—0
Cross Feature Fusion of Fundus Image and Generated Lesion Map for Referable Diabetic Retinopathy Classification—0
Leveraging Transfer Learning and Multiple Instance Learning for HER2 Automatic Scoring of H\&E Whole Slide ImagesCode0
Energy Price Modelling: A Comparative Evaluation of four Generations of Forecasting Methods—0
Exploiting the Segment Anything Model (SAM) for Lung Segmentation in Chest X-ray Images—0
A Mamba Foundation Model for Time Series Forecasting—0
Proxy-informed Bayesian transfer learning with unknown sources—0
Personalized Continual EEG Decoding: Retaining and Transferring Knowledge—0
Supervised Transfer Learning Framework for Fault Diagnosis in Wind Turbines—0
AM Flow: Adapters for Temporal Processing in Action Recognition—0
Against Multifaceted Graph Heterogeneity via Asymmetric Federated Prompt Learning—0
V-CAS: A Realtime Vehicle Anti Collision System Using Vision Transformer on Multi-Camera Streams—0
MultiBalance: Multi-Objective Gradient Balancing in Industrial-Scale Multi-Task Recommendation System—0
Transfer Learning for Finetuning Large Language Models—0
Magnitude Pruning of Large Pretrained Transformer Models with a Mixture Gaussian Prior—0
LLM-KT: A Versatile Framework for Knowledge Transfer from Large Language Models to Collaborative Filtering—0
Metric Learning for 3D Point Clouds Using Optimal Transport—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