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
Image registration is a fundamental problem in computer vision and robotics. Recently, learning-based image registration methods have made great progress. However, these methods are sensitive to abnormal transformation and have insufficient robustnes...
IEEE transactions on neural networks and learning systems
Jun 1, 2023
Event-based neural networks are currently being explored as efficient solutions for performing AI tasks at the extreme edge. To fully exploit their potential, event-based neural networks coupled to adequate preprocessing must be investigated. Within ...
Neural networks : the official journal of the International Neural Network Society
Apr 25, 2023
We prove new generalization bounds for stochastic gradient descent when training classifiers with invariances. Our analysis is based on the stability framework and covers both the convex case of linear classifiers and the non-convex case of homogeneo...
Neural networks : the official journal of the International Neural Network Society
Feb 15, 2023
Dimensional reduction (DR) maps high-dimensional data into a lower dimensions latent space with minimized defined optimization objectives. The two independent branches of DR are feature selection (FS) and feature projection (FP). FS focuses on select...
Neural networks : the official journal of the International Neural Network Society
Feb 4, 2023
Convolutional neural networks (CNNs) are often described as promising models of human vision, yet they show many differences from human abilities. We focus on a superhuman capacity of top-performing CNNs, namely, their ability to learn very large dat...
Neural networks : the official journal of the International Neural Network Society
Feb 3, 2023
The class of multi-relational graph convolutional networks (MRGCNs) is a recent extension of standard graph convolutional networks (GCNs) to handle heterogenous graphs with multiple types of relationships. MRGCNs have been shown to yield results supe...
Neural networks : the official journal of the International Neural Network Society
Feb 3, 2023
Matrix factorization has always been an encouraging field, which attempts to extract discriminative features from high-dimensional data. However, it suffers from negative generalization ability and high computational complexity when handling large-sc...
Neural networks : the official journal of the International Neural Network Society
Feb 2, 2023
The state of the art in violence detection in videos has improved in recent years thanks to deep learning models, but it is still below 90% of average precision in the most complex datasets, which may pose a problem of frequent false alarms in video ...
Neural networks : the official journal of the International Neural Network Society
Jan 20, 2023
In the imbalance data scenarios, Deep Neural Networks (DNNs) fail to generalize well on minority classes. In this letter, we propose a simple and effective learning function i.e, Visually Interpretable Space Adjustment Learning (VISAL) to handle the ...