GitHub

Cross-modal autoencoder (CMA) with clustering module

Generate a conda enviroment to run the model. conda create --name cma python=3.11 -c conda-forge conda activate cma pip install Jupyterlab matplotlib numpy pandas seaborn scikit-learn torch umap-learn ...
GitHub

Transfer-Learning-Based-Modular-Defense-Against-Natural-Adversarial-Examples

Our work introduces a novel defense approach against NAEs in the ImageNet dataset by leveraging a transfer learning-based defense module inserted before the final classification layer. Our method ...
IEEE

Improved AutoEncoder With LSTM Module and KL Divergence for Anomaly Detection

Abstract: The task of anomaly detection is to separate anomalous data from normal data in the dataset. Models such as deep Convolutional AutoEncoder (CAE) and deep support vector data description ...
IEEE

WGAN-based Autoencoder Training Over-the-air

"IEEE.tv is an excellent step by IEEE. This will pave a new way in knowledge-sharing and spreading ideas across the globe." ...
IEEE

MMEVAE: A Time-Varying Rolling Bearing Data Enhancement Method Based on a Multimodal Mechanism Enhanced Variational Autoencoder and Self-Attention Module

Abstract: Variational autoencoder (VAE) is widely used as a data enhancement technique. However, it faces challenges with inaccurate potential spatial distribution and poor reconstruction quality when ...
Visual Studio Magazine

Data Anomaly Detection Using a Neural Autoencoder with C#

Dr. James McCaffrey of Microsoft Research tackles the process of examining a set of source data to find data items that are different in some way from the majority of the source items. Data anomaly ...
Frontiers

Generation and discrimination of autism MRI images based on autoencoder

This study aims to explore an autoencoder-based method for generating brain MRI images of patients with Autism Spectrum Disorder (ASD) and non-ASD individuals, and to discriminate ASD based on the ...