Abstract: To address the issues of limited labeled data and the tendency of traditional supervised neural networks to overfit, resulting in low fault diagnosis accuracy in real industrial scenarios, ...

SVG Autoencoder - Uses a frozen representation encoder with a residual branch to compensate the information loss and a learned convolutional decoder to transfer the SVG latent space to pixel space.

Abstract: We introduce a new convolutional autoencoder architecture for user modeling and recommendation tasks with several improvements over the state of the art. First, our model has the flexibility ...

Chain information management system is widely used, providing convenience for the operation and management of enterprises. However, the problem of abnormal network traffic becomes increasingly ...

ABSTRACT: Convolutional auto-encoders have shown their remarkable performance in stacking deep convolutional neural networks for classifying image data during the past several years. However, they are ...

1 College of Information Engineering, Xinchuang Software Industry Base, Yancheng Teachers University, Yancheng, China. 2 Yancheng Agricultural College, Yancheng, China. Convolutional auto-encoders ...

Human inventions, namely engineered systems, have relied on fundamental discoveries in physics and mathematics, e.g., Maxwell’s equations, Quantum mechanics, Information theory, etc., thereby applying ...

Classification of power system event data is a growing need, particularly where non-protective relaying-based sensors are used to monitor grid performance. Given the high burden of obtaining event ...

Hyperspectral unmixing methods are essential to exploit the capabilities of Raman spectroscopy for nondestructive, unbiased chemical characterization in a wide array of domains, from biology, ...

Big Data Innovation and Entrepreneurship Education Center of Hunan Province, Changsha University, Changsha, China Introduction: Accumulating evidence shows that human health and disease are closely ...