Visual Studio Magazine

Autoencoder Anomaly Detection Using PyTorch

Anomaly detection is the process of finding items in a dataset that are different in some way from the majority of the items. For example, you could examine a dataset of credit card transactions to ...
Visual Studio Magazine

Autoencoder Anomaly Detection Using PyTorch

The Data Science Lab Autoencoder Anomaly Detection Using PyTorch Dr. James McCaffrey of Microsoft Research provides full code and step-by-step examples of anomaly detection, used to find items in a ...
GitHub

NVIDIA-DIGITS /examples /autoencoder

An image autoencoder may be used to learn a compressed representation of an image. An autoencoder comprises two parts: an encoder, which learns a representation of the image, using fewer neurons than ...
GitHub

05_kan_autoencoder.py

The bottleneck (32-dim) is the latent space.
IEEE

Contrastive Semantic-Aware Masked Autoencoder for Point Cloud Self-Supervised Learning

Abstract: Masked Autoencoder (MAE) has shown remarkable potential in self-supervised representation learning for 3D point clouds. However, these methods primarily rely on point-level or low-level ...
IEEE

Memory-Augmented Autoencoder With Adaptive Reconstruction and Sample Attribution Mining for Hyperspectral Anomaly Detection

Abstract: Hyperspectral anomaly detection (HAD) aims to identify targets that are significantly different from their surrounding background, employing an unsupervised paradigm. Recently, detectors ...
Microsoft

Crystal Diffusion Variational Autoencoder for Periodic Material Generation

Generating the periodic structure of stable materials is a long-standing challenge for the material design community. This task is difficult because stable materials only exist in a low-dimensional ...