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Visual transductive learning via iterative label correction
Unsupervised domain adaptation (UDA) aims to transfer knowledge across domains when there is no labeled data available in the target domain. In this...
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Iterative learning for maxillary sinus segmentation based on bounding box annotations
An accurate segmentation of the maxillary sinus (MS) is helpful for preoperative planning of dental implantation, diagnosis and evaluation of...
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Iterative and mixed-spaces image gradient inversion attack in federated learning
As a distributed learning paradigm, federated learning is supposed to protect data privacy without exchanging users’ local data. Even so, the gradient...
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Iterative cleaning and learning of big highly-imbalanced fraud data using unsupervised learning
Fraud datasets often times lack consistent and accurate labels, and are characterized by having high class imbalance where the number of fraudulent...
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A contrastive learning-based iterative network for remote sensing image super-resolution
Many deep convolutional neural network(CNN)-based methods have achieved significant success in noise-free image super-resolution(SR) tasks. However,...
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Spatial Adaptive Iterative Learning Control Based High-Speed Train Operation Tracking under External Disturbance
AbstractIn order to solve the trajectory tracking problem of high-speed train automatic operation systems under external random disturbance, the...
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Penalized Least Squares Classifier: Classification by Regression Via Iterative Cost-Sensitive Learning
Least squares estimate that can directly obtain the analytical solution to minimize the mean square error (MSE) is one of the most effective...
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Fault estimator design based on an iterative-learning scheme according to the forgetting factor for nonlinear systems
In this study, an iterative-learning-based fault estimator with the forgetting factor is proposed in response to the requirement of fault estimation...
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Quantized Iterative Learning Bipartite Containment Tracking Control for Unknown Nonlinear Multi-agent Systems
This paper proposes a quantized model-free adaptive iterative learning control (MFAILC) algorithm to solve the bipartite containment tracking problem...
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Research on Fuzzy Iterative Learning Control of Pneumatic Artificial Muscle
In order to achieve high-precision and rapid trajectory tracking control of pneumatic artificial muscle, a fuzzy iterative learning control scheme is...
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ICSMPC: Design of an Iterative-Learning Contextual Side Chaining Model for Improving Security of Priority-Aware Cloud Resources
Purpose: A wide variety of encryption-based, key-exchange-based, privacy-based, and confidentiality-based models are proposed by researchers, which...
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FP-GCN: fine pseudo-label driven iterative GCN to learning discriminative fusion features for unsupervised person re-identification
Unsupervised person re-identification (RE-ID) has attracted increasing attention recently due to its low costs and high application values....
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Adaptive Iterative Learning Control for Permanent Magnet Linear Synchronous Motor
For the parameters uncertainties and nonlinear disturbances for permanent magnet linear synchronous motor (PMLSM), an adaptive iterative learning...
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TimeLink: enabling dynamic runtime prediction for Flink iterative jobs
With the increasing growth of data scale and computing complexity, Flink, a novel distributed computing system, has been applied in various scenarios...
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Weakly Supervised Anomaly Detection Based on Two-Step Cyclic Iterative PU Learning Strategy
Weakly supervised anomaly detection for surveillance video is a highly challenging task due to inaccurate label information and sparse and diverse...
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Clustering Based Undersampling for Effective Learning from Imbalanced Data: An Iterative Approach
The class imbalance problem is prevalent in many classification tasks such as disease identification using microarray data, network intrusion...
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Low-dose CT iterative reconstruction based on image block classification and dictionary learning
For conventional image reconstruction based on dictionary learning in low-dose computed tomography (CT) imaging, all image blocks are represented by...
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Structural iterative lexicographic autoencoded node representation
Graph representation learning approaches are effective to automatically extract relevant hidden features from graphs. Previous related work in graph...
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A Novel Iterative Fusion Multi-task Learning Framework for Solving Dense Prediction
Dense prediction tasks are hot topics in computer vision that aim to predict each input image pixel, such as Semantic Segmentation, Monocular Depth...