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 9201–9250 of 10307 papers

TitleStatusHype
DeePref: Deep Reinforcement Learning For Video Prefetching In Content Delivery Networks—0
Deep Reinforcement Active Learning for Human-in-the-Loop Person Re-Identification—0
Deep Reinforcement Learning Based Cross-Layer Design in Terahertz Mesh Backhaul Networks—0
Deep Reinforcement Learning for Day-to-day Dynamic Tolling in Tradable Credit Schemes—0
Deep reinforcement learning for guidewire navigation in coronary artery phantom—0
Deep Reinforcement Learning for Multi-Agent Systems: A Review of Challenges, Solutions and Applications—0
Deep reinforcement learning for optical systems: A case study of mode-locked lasers—0
Deep Reinforcement Learning to Maximize Arterial Usage during Extreme Congestion—0
Semi-Supervised Domain Adaptation with Representation Learning for Semantic Segmentation across Time—0
Deep representation of EEG data from Spatio-Spectral Feature Images—0
Deep SIMBAD: Active Landmark-based Self-localization Using Ranking -based Scene Descriptor—0
Deep Speech Based End-to-End Automated Speech Recognition (ASR) for Indian-English Accents—0
Deep Speech Synthesis from Multimodal Articulatory Representations—0
Deep Stacking Networks for Low-Resource Chinese Word Segmentation with Transfer Learning—0
DeepStroke: An Efficient Stroke Screening Framework for Emergency Rooms with Multimodal Adversarial Deep Learning—0
Deep Subspace analysing for Semi-Supervised multi-label classification of Diabetic Foot Ulcer—0
Deep Transductive Transfer Learning for Automatic Target Recognition—0
Deep Transfer Clustering of Radio Signals—0
Deep Transfer Hashing for Adaptive Learning on Federated Streaming Data—0
Deep Transfer Learning: A new deep learning glitch classification method for advanced LIGO—0
Deep Transfer Learning: A Novel Collaborative Learning Model for Cyberattack Detection Systems in IoT Networks—0
Deep transfer learning for intrusion detection in industrial control networks: A comprehensive review—0
Deep Transfer Learning-Assisted Signal Detection for Ambient Backscatter Communications—0
Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks—0
Deep Transfer Learning Based Peer Review Aggregation and Meta-review Generation for Scientific Articles—0
Deep Transfer Learning & Beyond: Transformer Language Models in Information Systems Research—0
Deep Transfer Learning for Automatic Speech Recognition: Towards Better Generalization—0
Deep Transfer Learning for Brain Magnetic Resonance Image Multi-class Classification—0
Deep Transfer Learning for Cross-domain Activity Recognition—0
Deep transfer learning for detecting Covid-19, Pneumonia and Tuberculosis using CXR images -- A Review—0
Deep Transfer Learning for EEG-based Brain Computer Interface—0
Deep Transfer Learning for Error Decoding from Non-Invasive EEG—0
Deep Transfer Learning for Few-Shot SAR Image Classification—0
Deep Transfer Learning for Identifications of Slope Surface Cracks—0
Deep transfer learning for image classification: a survey—0
Deep transfer learning for improving single-EEG arousal detection—0
Deep Transfer Learning for Industrial Automation: A Review and Discussion of New Techniques for Data-Driven Machine Learning—0
Deep Transfer Learning for Infectious Disease Case Detection Using Electronic Medical Records—0
Deep Transfer Learning for Intelligent Vehicle Perception: a Survey—0
Deep Transfer Learning for Kidney Cancer Diagnosis—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
Deep Transfer Learning For Plant Center Localization—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
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
Deep Transfer Learning For Whole-Brain fMRI Analyses—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