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 9151–9200 of 10307 papers

TitleStatusHype
Deep Learning for automated multi-scale functional field boundaries extraction using multi-date Sentinel-2 and PlanetScope imagery: Case Study of Netherlands and Pakistan—0
Deep Learning for Automatic Quality Grading of Mangoes: Methods and Insights—0
Deep Learning for Automatic Stereotypical Motor Movement Detection using Wearable Sensors in Autism Spectrum Disorders—0
Deep Learning for Bias Detection: From Inception to Deployment—0
Deep Learning for Computational Cytology: A Survey—0
Deep Learning for Steganalysis of Diverse Data Types: A review of methods, taxonomy, challenges and future directions—0
Deep Learning for EEG Seizure Detection in Preterm Infants—0
Deep Learning for identifying radiogenomic associations in breast cancer—0
Deep Learning for Large-Scale Real-World ACARS and ADS-B Radio Signal Classification—0
Deep Learning for Lung Disease Classification Using Transfer Learning and a Customized CNN Architecture with Attention—0
Deep learning for model correction of dynamical systems with data scarcity—0
Deep Learning for Motion Classification in Ankle Exoskeletons Using Surface EMG and IMU Signals—0
Deep Learning for Over-the-Air Non-Orthogonal Signal Classification—0
Deep Learning for Radio Resource Allocation with Diverse Quality-of-Service Requirements in 5G—0
Deep Learning for Screening COVID-19 using Chest X-Ray Images—0
Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G Networks—0
Deep Learning in Earthquake Engineering: A Comprehensive Review—0
Deep Learning Innovations in Diagnosing Diabetic Retinopathy: The Potential of Transfer Learning and the DiaCNN Model—0
Deep learning lattice gauge theories—0
Deep Learning Method to Predict Wound Healing Progress Based on Collagen Fibers in Wound Tissue—0
Deep Learning Models for Classification of COVID-19 Cases by Medical Images—0
Deep learning models for gastric signet ring cell carcinoma classification in whole slide images—0
Deep Learning Models to Automate the Scoring of Hand Radiographs for Rheumatoid Arthritis—0
Deep Learning Model Transfer in Forest Mapping using Multi-source Satellite SAR and Optical Images—0
Deep Learning Object Detection Methods for Ecological Camera Trap Data—0
Deep Learning of Dynamic Subsurface Flow via Theory-guided Generative Adversarial Network—0
Deep Learning of Subsurface Flow via Theory-guided Neural Network—0
Deep Learning of Transferable MIMO Channel Modes for 6G V2X Communications—0
Deep Learning-Powered Classification of Thoracic Diseases in Chest X-Rays—0
Deep Learning Segmentation and Classification of Red Blood Cells Using a Large Multi-Scanner Dataset—0
Deep learning segmentation of fibrous cap in intravascular optical coherence tomography images—0
Deep Learning Techniques for Hand Vein Biometrics: A Comprehensive Review—0
Deep LSTM for Large Vocabulary Continuous Speech Recognition—0
Deep LSTM Spoken Term Detection using Wav2Vec 2.0 Recognizer—0
Deep machine learning for meteor monitoring: advances with transfer learning and gradient-weighted class activation mapping—0
Deep Maxout Network-based Feature Fusion and Political Tangent Search Optimizer enabled Transfer Learning for Thalassemia Detection—0
Deep Metric Learning for Few-Shot Image Classification: A Review of Recent Developments—0
DeepMI: Deep Multi-lead ECG Fusion for Identifying Myocardial Infarction and its Occurrence-time—0
Deep Molecular Programming: A Natural Implementation of Binary-Weight ReLU Neural Networks—0
Deep Multi-Instance Transfer Learning—0
Deep Multilabel CNN for Forensic Footwear Impression Descriptor Identification—0
Deep Multi-Task Learning to Recognise Subtle Facial Expressions of Mental States—0
Deep Neural Network-Based Sign Language Recognition: A Comprehensive Approach Using Transfer Learning with Explainability—0
Deep Neural Network Models Compression—0
Deep neural network models for computational histopathology: A survey—0
Deep Neural Network Models Trained With A Fixed Random Classifier Transfer Better Across Domains—0
Deep Neural Networks to Enable Real-time Multimessenger Astrophysics—0
DeepONet as a Multi-Operator Extrapolation Model: Distributed Pretraining with Physics-Informed Fine-Tuning—0
Deep Q-Learning with Q-Matrix Transfer Learning for Novel Fire Evacuation Environment—0
Deep Radiomics for Brain Tumor Detection and Classification from Multi-Sequence MRI—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