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 7601–7625 of 10307 papers

TitleStatusHype
REPAINT: Knowledge Transfer in Deep Actor-Critic Reinforcement Learning—0
Sense and Learn: Self-Supervision for Omnipresent Sensors—0
Transfer among Agents: An Efficient Multiagent Transfer Learning Framework—0
Smart Irrigation IoT Solution using Transfer Learning for Neural Networks—0
Federated Transfer Learning: concept and applications—0
Generating Realistic COVID19 X-rays with a Mean Teacher + Transfer Learning GAN—0
Democratizing Artificial Intelligence in Healthcare: A Study of Model Development Across Two Institutions Incorporating Transfer Learning—0
Automatic identification of fossils and abiotic grains during carbonate microfacies analysis using deep convolutional neural networksCode0
A Computer Vision Approach to Combat Lyme Disease—0
Privacy-preserving Transfer Learning via Secure Maximum Mean Discrepancy—0
Transfer Learning by Cascaded Network to identify and classify lung nodules for cancer detection—0
Worst-Case-Aware Curriculum Learning for Zero and Few Shot TransferCode0
An Attention Mechanism with Multiple Knowledge Sources for COVID-19 Detection from CT Images—0
Steering a Historical Disease Forecasting Model Under a Pandemic: Case of Flu and COVID-19Code0
Classification of COVID-19 in CT Scans using Multi-Source Transfer LearningCode0
Deep Learning Applied to Chest X-Rays: Exploiting and Preventing Shortcuts—0
Generative Imagination Elevates Machine Translation—0
Improving Automated COVID-19 Grading with Convolutional Neural Networks in Computed Tomography Scans: An Ablation Study—0
WESSA at SemEval-2020 Task 9: Code-Mixed Sentiment Analysis using Transformers—0
MARS: Mixed Virtual and Real Wearable Sensors for Human Activity Recognition with Multi-Domain Deep Learning Model—0
Open-Ended Fine-Grained 3D Object Categorization by Combining Shape and Texture Features in Multiple Colorspaces—0
Knowledge Transfer via Pre-training for Recommendation: A Review and Prospect—0
Face Mask Detection using Transfer Learning of InceptionV3—0
Automated Source Code Generation and Auto-completion Using Deep Learning: Comparing and Discussing Current Language-Model-Related ApproachesCode0
Collaborative Group 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