We proposed a convolutional autoencoder with sequential and channel attention (CAE-SCA) to address this issue. Sequential attention (SA) is based on long short-term memory (LSTM), which captures ...

Our research dives into the performance comparison of two popular machine learning approaches: the support vector machine (SVM) and the more recent deep learning-based stacked autoencoder (SAE). We ...

Accurate prediction of protein subcellular localization is critical for understanding cellular functions and guiding drug design. However, current computational methods have limited and insufficient ...

Hyperspectral X-ray analysis is used in many industrial pipelines, from quality control to detection of low-density contaminants in food. Unfortunately, the signal acquired by X-ray sensors is often ...

In this paper, we introduce MaeFuse, a novel autoencoder model designed for Infrared and Visible Image Fusion (IVIF). The existing approaches for image fusion often rely on training combined with ...

AUTOVC is a voice-conversion method that performs self-reconstruction using an autoencoder structure for zero-shot voice conversion. AUTOVC has the advantage of being easy and simple to learn because ...

Masked Autoencoder (MAE) has shown remarkable potential in self-supervised representation learning for 3D point clouds. However, these methods primarily rely on point-level or low-level feature ...

Machine learning models have found their applications in solving problems that can traditionally handle ordinary or partial differential equations. The full-wave simulations solve such equations for ...

In this study, we propose a fault detection algorithm based on an autoencoder-based classification model. Unlike traditional neural networks, autoencoders have equal numbers of neurons in the input ...

To apply emotion recognition and classification technology to the field of human-robot interaction, it is necessary to implement fast data processing and model weight reduction. This paper proposes a ...

Therefore, this article proposes a deeply integrated autoencoder anomaly detection method for anomaly detection and critical parameter identification of UAV actuators. First, to reduce the influence ...

We propose a Crystal Diffusion Variational Autoencoder (CDVAE) that captures the physical inductive bias of material stability. By learning from the data distribution of stable materials, the decoder ...