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 ...
IEEE

Dropout Concrete Autoencoder for Band Selection on Hyperspectral Image Scenes

Abstract: Deep learning-based informative band selection methods on hyperspectral images (HSIs) have recently gained intense attention to eliminate spectral correlation and redundancies. However, ...
GitHub

Prometheus Time Series Anomaly Detection with LSTM Autoencoder

This project implements a system for detecting anomalies in time series data collected from Prometheus. It uses an LSTM (Long Short-Term Memory) autoencoder model built with TensorFlow/Keras to learn ...