Neural networks : the official journal of the International Neural Network Society
Jun 30, 2023
Deep neural networks are sensitive to adversarial examples and would produce wrong results with high confidence. However, most existing attack methods exhibit weak transferability, especially for adversarially trained models and defense models. In th...
Neural networks : the official journal of the International Neural Network Society
Jun 30, 2023
Graph Convolutional Networks (GCNs) with naive message passing mechanisms have limited performance due to the isotropic aggregation strategy. To remedy this drawback, some recent works focus on how to design anisotropic aggregation strategies with tr...
Neural networks : the official journal of the International Neural Network Society
Jun 30, 2023
The single image de-snowing task is an essential topic in computer vision, as images captured on snowy days degrade the performance of current vision-based intelligent systems. Existing methods build complex network structures with numerous parameter...
Neural networks : the official journal of the International Neural Network Society
Jun 30, 2023
Recent works have demonstrated the superiority of supervised Convolutional Neural Networks (CNNs) in learning hierarchical representations from time series data for successful classification. These methods require sufficiently large labeled data for ...
Neural networks : the official journal of the International Neural Network Society
Jun 30, 2023
In this paper, we investigate a novel framework for achieving prescribed-time (PAT), fixed-time (FXT) and finite-time (FNT) stochastic synchronization control of semi-Markov switching quaternion-valued neural networks (SMS-QVNNs), where the setting t...
IEEE transactions on pattern analysis and machine intelligence
Jun 30, 2023
Normalization techniques are essential for accelerating the training and improving the generalization of deep neural networks (DNNs), and have successfully been used in various applications. This paper reviews and comments on the past, present and fu...
IEEE transactions on pattern analysis and machine intelligence
Jun 30, 2023
We present compact and effective deep convolutional neural networks (CNNs) by exploring properties of videos for video deblurring. Motivated by the non-uniform blur property that not all the pixels of the frames are blurry, we develop a CNN to integr...
IEEE transactions on pattern analysis and machine intelligence
Jun 30, 2023
Availability of labelled data is the major obstacle to the deployment of deep learning algorithms for computer vision tasks in new domains. The fact that many frameworks adopted to solve different tasks share the same architecture suggests that there...
IEEE transactions on pattern analysis and machine intelligence
Jun 30, 2023
Image restoration under adverse weather conditions has been of significant interest for various computer vision applications. Recent successful methods rely on the current progress in deep neural network architectural designs (e.g., with vision trans...
IEEE transactions on pattern analysis and machine intelligence
Jun 30, 2023
Deep learning for change detection is one of the current hot topics in the field of remote sensing. However, most end-to-end networks are proposed for supervised change detection, and unsupervised change detection models depend on traditional pre-det...