IEEE

XChange: An Explainable Dynamic Convolutional Autoencoder for Unsupervised Change Detection

Abstract: Explainable artificial intelligence (XAI) approaches started to be studied in the last period to improve the interpretability of increasingly complex deep learning (DL) methods for remote ...
IEEE

Fault Detection Method for Power Conversion Circuits Using Thermal Image and Convolutional Autoencoder

A fault detection method for power conversion circuits using thermal images and a convolutional autoencoder is presented. The autoencoder is trained on thermal images captured from a commercial power ...
GitHub

vae-cnn

PyTorch-based pipeline that trains a convolutional variational autoencoder on cat images, optionally tunes hyperparameters with Ray Tune, and samples new images by fitting a Gaussian Mixture Model in ...
GitHub

autoencoder-mnist

Image Denoising with Autoencoders in R (University Project) Built a convolutional autoencoder in R using Keras/TensorFlow to perform image denoising on MNIST and CIFAR-10 datasets with varying levels ...
Oak Ridge National Lab

Convolutional Variational Autoencoder-based Unsupervised Learning for Power Systems Faults

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 ...