Abstract: Catastrophic forgetting is the core problem of class incremental learning (CIL). Existing work mainly adopts memory replay, knowledge distillation, and dynamic architecture to alleviate this ...

Abstract: Quantum Federated Learning (QFL) recently becomes a promising approach with the potential to revolutionize Machine Learning (ML). It merges the established strengths of classical Federated ...

Abstract: Fault diagnosis of railway assets has drawn the interest of both the scholarly and engineering communities. Federated learning (FL) enables training models across distributed assets to ...

Abstract: For industrial batch processes with unknown dynamics subject to nonrepetitive initial conditions and disturbances, this article develops a novel adaptive data-driven set-point learning ...

Abstract: Self-supervised point cloud representation learning aims to acquire robust and general feature representations from unlabeled data. Recently, masked point modeling-based methods have shown ...

Abstract: Navigating a nonholonomic robot in a cluttered, unknown environment requires accurate perception and precise motion control for real-time collision avoidance. This article presents neural ...

Abstract: Self-supervised learning of point cloud aims to leverage unlabeled 3D data to learn meaningful representations without reliance on manual annotations. However, current approaches face ...

Abstract: Weakly supervised semantic segmentation methods can effectively alleviate the problem of high cost and difficult access to annotation in traditional methods. Among these approaches, point ...

This module gives a demo on Abstract Factory Design Pattern in JAVA with its implementation code, why to use it, where to use it, advantages & disadvantages, how to implement, etc ...