11 hours ago · The architecture of the CAE model is based on a convolutional autoencoder (CAE) proposed in Ref. . It consists of an encoder with convolutional layers that compresses the image to a lower dimensional latent space, and a decoder with deconvolutional layers that reconstructs the image from the latent space to the original input dimensionality.

11 hours ago · Convolutional Autoencoder Model The architecture of the CAE model is based on a convolutional autoencoder (CAE) proposed in Ref. [20]. It consists of an encoder with convolutional layers that compresses ... used to study the anomaly detection performance; from left to right, background, Sahara dust storm, dead pixel, and hot pixel images ...

In this paper, we propose a hybrid autoencoder model, called ConvBiLSTM-AE, which combines convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) to more effectively train complex temporal data patterns in anomaly detection.

2 days ago · Abstract page for arXiv paper 2505.00040: Convolutional Autoencoders for Data Compression and Anomaly Detection in Small Satellite Technologies. ... This work presents convolutional autoencoders for implementation on the payload of small satellites, designed to achieve dual functionality of data compression for more efficient off-satellite ...

In this paper we investigate unsupervised anomaly detection for 2-dimensional data in manufacturing environment: we provide an approach that exploit Deep Learning-based architecture for handling the data at hand.

12 hours ago · This study focuses on anomaly detection in X-ray images of paintings using the Ghent Altarpiece for training and testing purposes. ... The authors used the convolutional autoencoder to decompose ...

Sep 1, 2024 · By exploring image-based anomaly detection, our analysis aims to provide insights into the effectiveness of Auto-Encoder models in this domain to propose further improvements.

Jan 1, 2021 · We designed two anomaly detectors - an Adversarial Autoencoder (AAE) and a Deep Convolutional Generative Adversarial Networks (DCGAN). These models are build up on models from resources Autoencoders (2020) and Deep (2020). Networks are trained using picture datasets MNIST, Fashion-MNIST and CIFAR10.

Apr 25, 2025 · Anomaly detection has also been comprehensively reviewed for intrusion detection in previous literature [11, 12]. Detecting anomalous system behaviours in ICSs is a common problem in smart industrial applications. A range of anomaly detection algorithms have been proposed and applied within this application domain in previous work.

Jan 10, 2025 · Convolutional Autoencoders: These use convolutional layers instead of fully connected layers. They're particularly good for image data. How to Train an Autoencoder for Anomaly Detection. Alright, let's get into the nitty-gritty. Here's a step-by-step guide to training an autoencoder for anomaly detection: Step 1: Data Preparation