WACV 2020 paper: Reverse Variational Autoencoder for Visual Attribute Manipulation and Anomaly Detection Pytorch Implementation - nianlonggu/reverse-variational-autoencoder ...

This repository contains the presentation for the paper "Disentangled Conditional Variational Autoencoder (dCVAE) for Unsupervised Anomaly Detection", presented at IEEE BigData 2024. The paper is ...

Masked Autoencoder (MAE) has demonstrated superior performance on various vision tasks via randomly masking image patches and reconstruction. However, effective data augmentation strategies for MAE ...

An autoencoder (Masci et al., 2011) is an unsupervised model that is trained to reconstruct the input from extracted features. This is similar to PCA, but an autoencoder can be composed of multiple ...

Unsupervised learning approaches to vein recognition algorithms are approaching the state of the art, and could soon reach it, according to the latest lunch talk from the European Association for ...

Keywords: visual SLAM, loop closure detection, variational autoencoder, attention mechanism, loss function. Citation: Song S, Yu F, Jiang X, Zhu J, Cheng W and Fang X (2024) Loop closure detection of ...

Visual Servoing in Autoencoder Latent Space Abstract: Visual servoing (VS) is a common way in robotics to control a robot motion usinginformation acquired by a camera. This approach requires to ...

Normalizing and Encoding Source Data for an Autoencoder In practice, preparing the source data for an autoencoder is the most time-consuming part of the dimensionality reduction process. To normalize ...

Data Dimensionality Reduction Using a Neural Autoencoder with C#. Dr. James McCaffrey of Microsoft Research presents a full-code, step-by-step tutorial on creating an approximation of a dataset that ...