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 2926–2950 of 10307 papers

TitleStatusHype
Detection of COVID19 in Chest X-Ray Images Using Transfer Learning—0
Beyond Flatland: Pre-training with a Strong 3D Inductive Bias—0
Detection of Hate Speech using BERT and Hate Speech Word Embedding with Deep Model—0
Beyond Glucose-Only Assessment: Advancing Nocturnal Hypoglycemia Prediction in Children with Type 1 Diabetes—0
Analysis of the Two-Step Heterogeneous Transfer Learning for Laryngeal Blood Vessel Classification: Issue and Improvement—0
Detection of Plant Leaf Disease Directly in the JPEG Compressed Domain using Transfer Learning Technique—0
Detection under Privileged Information—0
Detector Discovery in the Wild: Joint Multiple Instance and Representation Learning—0
Determination of droplet size from wide-angle light scattering image data using convolutional neural networks—0
A Data-Driven Evolutionary Transfer Optimization for Expensive Problems in Dynamic Environments—0
Deep-Learning Driven Noise Reduction for Reduced Flux Computed Tomography—0
DetOFA: Efficient Training of Once-for-All Networks for Object Detection Using Path Filter—0
``Deep'' Learning : Detecting Metaphoricity in Adjective-Noun Pairs—0
Developing Conversational Data and Detection of Conversational Humor in Telugu—0
Developing efficient transfer learning strategies for robust scene recognition in mobile robotics using pre-trained convolutional neural networks—0
Developing High Quality Training Samples for Deep Learning Based Local Climate Zone Classification in Korea—0
Autonomous learning of multiple, context-dependent tasks—0
Deep-Learning Convolutional Neural Networks for scattered shrub detection with Google Earth Imagery—0
Autonomous Extraction of a Hierarchical Structure of Tasks in Reinforcement Learning, A Sequential Associate Rule Mining Approach—0
A Data-Driven Approach to Improve 3D Head-Pose Estimation—0
Driving Tasks Transfer in Deep Reinforcement Learning for Decision-making of Autonomous Vehicles—0
Deep learning-based Visual Measurement Extraction within an Adaptive Digital Twin Framework from Limited Data Using Transfer Learning—0
Development of a TV Broadcasts Speech Recognition System for Qatari Arabic—0
Development of a WAZOBIA-Named Entity Recognition System—0
Deep learning-based variational autoencoder for classification of quantum and classical states of light—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