Autoencoders is to ignore anomalies and minimize the reconstruction error on normal data. The goal of this work is to investigate approaches to allow reconstruction error-based architectures …

Variational autoencoder (VAE) [3] is a generative model widely used in image reconstruction and generation tasks. It provides a more efficient way (e.g. in comparison to a standard …

Our proposed method, AEROB- LADE (autoencoder reconstruction-based latent diffusion detection), distinguishes real and generated images by com-puting their AE reconstruction …

In this paper, we propose a theoretically sound deep architecture, named reversible autoencoder (Rev-AE), from the perspective of well-developed frame theory for image reconstruction.

Push it to the Limit: Discover Edge-Cases in Image Data with Autoencoders; Walking the Tightrope: An Investigation of the Convolutional Autoencoder Bottleneck; To sum it up, …

Sep 26, 2023 · During production, each newly manufactured chip image is passed through the autoencoder. The model attempts to reconstruct the chip image, and the reconstruction error …

Jun 29, 2021 · This paper aims for image reconstruction of autoencoders, employs cascade decoders-based autoencoders, perfects the performance of image reconstruction, approaches …

CNN autoencoder is trained on the MNIST numbers dataset for image reconstruction. Anomaly detection is carried out by calculating the Z-score. The framework used is Keras.

Aug 31, 2023 · In a data-driven world - optimizing its size is paramount. Autoencoders automatically encode and decode information for ease of transport. In this article, we'll be …

Jun 16, 2017 · When I am encoding and decoding over the test set, how do I calculate the reconstruction error (i.e. the Mean Squared Error/Loss) for each sample? In other words I'd …