The health indicator (HI) of rotating machinery affects the reliability and accuracy of its remaining useful life (RUL) prediction. Convolutional autoencoder (CAE) is widely used for HI construction ...

Blind hyperspectral unmixing is the process of expressing the measured spectrum of a pixel as a combination of a set of spectral signatures called endmembers and simultaneously determining their ...

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.

However, some PS methods have spectral and spatial distortions that influence subsequent analyses. Thus, this study aimed to develop a PS method based on convolutional autoencoder (CAE) for Landsat 8 ...

PyTorch implementations of an Undercomplete Autoencoder and a Denoising Autoencoder that learns a lower dimensional latent space representation of images from the MNIST dataset.

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 ...

Dr. James McCaffrey of Microsoft Research provides full code and step-by-step examples of anomaly detection, used to find items in a dataset that are different from the majority for tasks like ...