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 2826–2850 of 10307 papers

TitleStatusHype
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