Self-supervised Human Activity Recognition (HAR) has been gradually gaining a lot of attention in ubiquitous computing community. Its current focus primarily lies in how to overcome the challenge of ...

In this article, we present a new spectral-spatial linear mixture model and an associated estimation method based on a convolutional neural network autoencoder unmixing (CNNAEU). The CNNAEU technique ...

In this paper, the stacked convolutional autoencoder network structure constructed with fusion selection kernel attention mechanism is based on FCAE, which consists of an encoder and a decoder.

The source code for the arXiv paper titled "KAE: Kolmogorov-Arnold Auto-Encoder for Representation Learning" is available. This code, created by Ruilizhen Hu, Fangchen Yu, and Yidong Lin, supports ...

Finally, we propose the best configuration and settings for the hyperparameters. The main contributions are summarized as follows: • A convolutional autoencoder model that can perform the endmember ...

Convolutional autoencoder, domain adaptation, and shallow classifiers. We first separately applies NMF on MIMIC and CHOA data for feature dimensionality reduction, then used two separate CAE models to ...

In this study, a deep convolutional neural network with deconvolution and a deep autoencoder (DDD) was proposed for the construction of an MSPC-based deep neural network that assesses the process ...