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 2801–2850 of 10307 papers

TitleStatusHype
An Automatic SOAP Classification System Using Weakly Supervision And Transfer Learning—0
A Comprehensive Overview and Comparative Analysis on Deep Learning Models: CNN, RNN, LSTM, GRU—0
Deep Learning for Steganalysis of Diverse Data Types: A review of methods, taxonomy, challenges and future directions—0
Deep Transfer-Learning for patient specific model re-calibration: Application to sEMG-Classification—0
Deep Transfer Learning for Person Re-identification—0
Forecasting adverse surgical events using self-supervised transfer learning for physiological signals—0
Analysis Towards Classification of Infection and Ischaemia of Diabetic Foot Ulcers—0
Deep Transfer Learning for Signal Detection in Ambient Backscatter Communications—0
Deep Transfer Learning for Single-Channel Automatic Sleep Staging with Channel Mismatch—0
Bayesian multi-domain learning for cancer subtype discovery from next-generation sequencing count data—0
Addressing Dynamic and Sparse Qualitative Data: A Hilbert Space Embedding of Categorical Variables—0
Deep transfer learning for system identification using long short-term memory neural networks—0
Deep Transfer Learning for Texture Classification in Colorectal Cancer Histology—0
Deep Transfer Learning for Thermal Dynamics Modeling in Smart Buildings—0
Domain Adaptation for Visual Applications: A Comprehensive Survey—0
Domain adaptation in practice: Lessons from a real-world information extraction pipeline—0
Deep Transfer Learning for WiFi Localization—0
Bayesian Optimization of Bilevel Problems—0
Deep Learning for Computational Cytology: A Survey—0
Deep Transfer Learning Methods for Colon Cancer Classification in Confocal Laser Microscopy Images—0
Deep Transfer Learning: Model Framework and Error Analysis—0
Deep Transfer Learning on Satellite Imagery Improves Air Quality Estimates in Developing Nations—0
Deep Transfer Learning with Graph Neural Network for Sensor-Based Human Activity Recognition—0
Deep Transfer Learning with Joint Adaptation Networks—0
Deep Transfer Learning with Ridge Regression—0
Deep Transfer Network with Joint Distribution Adaptation: A New Intelligent Fault Diagnosis Framework for Industry Application—0
Deep Transfer Q-Learning for Offline Non-Stationary Reinforcement Learning—0
Bayesian Transfer Reinforcement Learning with Prior Knowledge Rules—0
Auto-Transfer: Learning to Route Transferable Representations—0
A Comparison of Self-Supervised Pretraining Approaches for Predicting Disease Risk from Chest Radiograph Images—0
Deep Variational Transfer: Transfer Learning through Semi-supervised Deep Generative Models—0
DeepVesselNet: Vessel Segmentation, Centerline Prediction, and Bifurcation Detection in 3-D Angiographic Volumes—0
DeepVIVONet: Using deep neural operators to optimize sensor locations with application to vortex-induced vibrations—0
BCNet: A Deep Convolutional Neural Network for Breast Cancer Grading—0
Deep Zero-Shot Learning for Scene Sketch—0
DeeSIL: Deep-Shallow Incremental Learning—0
DEEVA: A Deep Learning and IoT Based Computer Vision System to Address Safety and Security of Production Sites in Energy Industry—0
Deep Learning for Bias Detection: From Inception to Deployment—0
Defect Analysis of 3D Printed Cylinder Object Using Transfer Learning Approaches—0
Defeating Misclassification Attacks Against Transfer Learning—0
Defense against adversarial attacks on deep convolutional neural networks through nonlocal denoising—0
Defining Boundaries: The Impact of Domain Specification on Cross-Language and Cross-Domain Transfer in Machine Translation—0
Defining Image Memorability using the Visual Memory Schema—0
Deformable-Detection Transformer for Microbubble Localization in Ultrasound Localization Microscopy—0
Behavior Priors for Efficient Reinforcement Learning—0
DeFungi: Direct Mycological Examination of Microscopic Fungi Images—0
Domain Adaptation for Robot Predictive Maintenance Systems—0
Deep Learning for Automatic Stereotypical Motor Movement Detection using Wearable Sensors in Autism Spectrum Disorders—0
Deep Learning for Automatic Quality Grading of Mangoes: Methods and Insights—0
AutoSync: Learning to Synchronize for Data-Parallel Distributed Deep 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