Anomaly detection is crucial in areas such as financial fraud identification, cybersecurity defense, and health monitoring, as it directly affects the accuracy and security of decision-making. Existing generative adversarial nets (GANs)-based anomaly...
Neural networks : the official journal of the International Neural Network Society
Jan 22, 2025
Reconstruction-based methods achieve promising performance for visual anomaly detection (AD), relying on the underlying assumption that the anomalies cannot be accurately reconstructed. However, this assumption does not always hold, especially when s...
Neural networks : the official journal of the International Neural Network Society
Jan 22, 2025
Unsupervised domain adaptation (UDA) aims to annotate unlabeled target domain samples using transferable knowledge learned from the labeled source domain. Optimal transport (OT) is a widely adopted probability metric in transfer learning for quantify...
Small (Weinheim an der Bergstrasse, Germany)
Jan 21, 2025
Spiking neurons are essential for building energy-efficient biomimetic spatiotemporal systems because they communicate with other neurons using sparse and binary signals. However, the achievable high density of artificial neurons having a capacitor f...
The inherent variability of lesions poses challenges in leveraging AI in 3D automated breast ultrasound (ABUS) for lesion detection. Traditional methods based on single scans have fallen short compared to comprehensive evaluations by experienced sono...
Neural networks : the official journal of the International Neural Network Society
Jan 17, 2025
Due to data privacy and storage concerns, Source-Free Unsupervised Domain Adaptation (SFUDA) focuses on improving an unlabelled target domain by leveraging a pre-trained source model without access to source data. While existing studies attempt to tr...
In credit risk assessment, unsupervised classification techniques can be introduced to reduce human resource expenses and expedite decision-making. Despite the efficacy of unsupervised learning methods in handling unlabeled datasets, their performanc...
Neural networks : the official journal of the International Neural Network Society
Jan 8, 2025
Conditional adversarial domain adaptation (CADA) is one of the most commonly used unsupervised domain adaptation (UDA) methods. CADA introduces multimodal information to the adversarial learning process to align the distributions of the labeled sourc...
In recent decades, medical image registration technology has undergone significant development, becoming one of the core technologies in medical image analysis. With the rise of deep learning, deep learning-based medical image registration methods ha...
IEEE transactions on neural networks and learning systems
Jan 7, 2025
Medical image segmentation is an important task in medical imaging, as it serves as the first step for clinical diagnosis and treatment planning. While major success has been reported using deep learning supervised techniques, they assume a large and...
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