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
Deep learning lattice gauge theories—0
A Vision-based Solution for Track Misalignment Detection—0
Analyzing Knowledge Transfer in Deep Q-Networks for Autonomously Handling Multiple Intersections—0
Deep Learning Models for Classification of COVID-19 Cases by Medical Images—0
Deep learning for model correction of dynamical systems with data scarcity—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 for Lung Disease Classification Using Transfer Learning and a Customized CNN Architecture with Attention—0
Deep Learning of Transferable MIMO Channel Modes for 6G V2X Communications—0
A Dataset for Sanskrit Word Segmentation—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 Learning for Large-Scale Real-World ACARS and ADS-B Radio Signal Classification—0
Deep Learning for identifying radiogenomic associations in breast cancer—0
Auxiliary Input in Training: Incorporating Catheter Features into Deep Learning Models for ECG-Free Dynamic Coronary Roadmapping—0
Analyzing Customer Feedback for Product Fit Prediction—0
A white-box analysis on the writer-independent dichotomy transformation applied to offline handwritten signature verification—0
Model-Based and Data-Driven Strategies in Medical Image Computing—0
Deep Maxout Network-based Feature Fusion and Political Tangent Search Optimizer enabled Transfer Learning for Thalassemia Detection—0
Discriminative Partial Domain Adversarial Network—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