Unsupervised Domain Adaptive Segmentation Algorithm Based on Two-Level Category Alignment

Unsupervised Domain Adaptive Segmentation Algorithm Based on Two-Level Category Alignment

Information Science, Automation, Artificial Intelligence,
unsupervised domain adaption   semantic segmentation   two-level category alignment   class activation map   generative adversarial network  

Semantic segmentation aims to predict category labels for each pixel in an image (Liu et al., 2021; Wang et al., 2021) and is widely used in scene understanding, medical image analysis, autonomous driving, geographic information systems, and augmented reality (Strudel et al., 2021; Sun et al., 2023). Although the development of deep neural networks...

Prototype-Based Sample-Weighted Distillation Unified Framework Adapted to Missing Modality Sentiment Analysis

Prototype-Based Sample-Weighted Distillation Unified Framework Adapted to Missing Modality Sentiment Analysis

Information Science, Automation, Artificial Intelligence, Humanities and Others,
missing modality   sentiment analysis   knowledge distillation   prototype network   multimodal optimization  

Application of a Prototype-Based Sample Weighted Distillation Unified Framework in Missing Modality Sentiment Analysis Research Background Sentiment analysis is a significant field in Natural Language Processing (NLP). With the development of social media platforms, people increasingly tend to express their emotions through short video clips. This ...

Efficient Tensor Decomposition-Based Filter Pruning

Information Science, Automation, Artificial Intelligence,
network compression   tensor decompositions   filter pruning   convolutional neural networks   low-rank approximation  

Background Introduction Network Pruning is a crucial technique for designing efficient Convolutional Neural Network (CNN) models. By reducing memory footprint and computational demands, while maintaining or improving overall performance, it makes deploying CNNs on resource-constrained devices (such as mobile phones or embedded systems) feasible. Th...

A Robust Multi-Scale Feature Extraction Framework with Dual Memory Module for Multivariate Time Series Anomaly Detection

A Robust Multi-Scale Feature Extraction Framework with Dual Memory Module for Multivariate Time Series Anomaly Detection

Information Science, Automation, Artificial Intelligence,
anomaly detection   multivariate time series   robust feature extraction   multi-scale   global-local memory module  

With the rapid development of deep learning technology, the importance of data mining and artificial intelligence training techniques in practical applications has become increasingly prominent. Especially in the field of multivariate time series anomaly detection, existing methods, though excellent, still face significant issues when dealing with ...

Active Dynamic Weighting for Multi-Domain Adaptation

Information Science, Artificial Intelligence,
multi-source domain adaptation   transfer learning   active learning   distribution alignment   dynamic boundary loss  

Background Introduction Multi-source Unsupervised Domain Adaptation (MUDA) aims to transfer knowledge from multiple labeled source domains to an unlabeled target domain. However, existing methods often merely seek to blend distributions between different domains or combine multiple single-source models in the decision process through weighted fusio...

Sliding Mode Control for Uncertain Fractional-Order Reaction-Diffusion Memristor Neural Networks with Time Delays

Physics, Electromagnetism, Information Science, Artificial Intelligence,
sliding mode control   fractional-order   time delays   reaction-diffusion   memristor   neural networks  

Application of Sliding Mode Control in Uncertain Fractional-Order Reaction-Diffusion Memristor Neural Networks In recent years, as neural networks have been widely applied in various fields, the research on their control and stability has gained increasing attention. Fractional-order (FO) memristor neural networks (MNNs), due to their ability to si...