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 2701–2725 of 10307 papers

TitleStatusHype
Lifelong Learning for Fog Load Balancing: A Transfer Learning Approach—0
Deep Reinforcement Learning Based Cross-Layer Design in Terahertz Mesh Backhaul Networks—0
Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps DomainCode1
Comparative Analysis of Transfer Learning in Deep Learning Text-to-Speech Models on a Few-Shot, Low-Resource, Customized Dataset—0
Enhancing Cross-Dataset Performance of Distracted Driving Detection With Score Softmax Classifier And Dynamic Gaussian Smoothing SupervisionCode0
Transferable Deep Clustering Model—0
Tight Rates in Supervised Outlier Transfer Learning—0
CAD Models to Real-World Images: A Practical Approach to Unsupervised Domain Adaptation in Industrial Object ClassificationCode0
X-Transfer: A Transfer Learning-Based Framework for GAN-Generated Fake Image Detection—0
EdgeFD: An Edge-Friendly Drift-Aware Fault Diagnosis System for Industrial IoT—0
Acoustic and linguistic representations for speech continuous emotion recognition in call center conversations—0
Robust Transfer Learning with Unreliable Source Data—0
Transferring speech-generic and depression-specific knowledge for Alzheimer's disease detection—0
Amortized Network Intervention to Steer the Excitatory Point Processes—0
Enhancing the Authenticity of Rendered Portraits with Identity-Consistent Transfer Learning—0
Assessing Electricity Service Unfairness with Transfer Counterfactual Learning—0
ECAvg: An Edge-Cloud Collaborative Learning Approach using Averaged Weights—0
T-GAE: Transferable Graph Autoencoder for Network AlignmentCode0
LumiNet: The Bright Side of Perceptual Knowledge DistillationCode1
Developing a Novel Holistic, Personalized Dementia Risk Prediction Model via Integration of Machine Learning and Network Systems Biology Approaches—0
A Survey of GPT-3 Family Large Language Models Including ChatGPT and GPT-4—0
Learning to Prompt Your Domain for Vision-Language Models—0
Crossed-IoT device portability of Electromagnetic Side Channel Analysis: Challenges and Dataset—0
Progressive reduced order modeling: empowering data-driven modeling with selective knowledge transfer—0
Comparative Analysis of Imbalanced Malware Byteplot Image Classification using 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