To capture the latent spatial features of anomaly detection, we present an unsupervised latent feature autoencoder via adversarial training.

To capture the latent spatial features of anomaly detection, we present an un- supervised latent feature autoencoder via adversarial training. Particularly, we ...

To capture the latent spatial features of anomaly detection, we present an unsupervised latent feature autoencoder via adversarial training. Particularly, we ...

Nearest neighbors is a successful and long-standing technique for anomaly detection. Significant progress has been recently achieved by self-supervised deep ...

A latent feature autoencoder via adversarial training for unsupervised anomaly detection. W Tang, J Li. 2021 IEEE International Conference on Systems, Man ...

Jul 13, 2023 · We propose a new UAD framework by introducing a Latent Feature Reconstruction (LFR) layer that can be applied to recent UAD methods.

This work introduces a novel anomaly detection model, by using a conditional generative adversarial network that jointly learns the generation of ...

This study systematically reviews 11 Auto-Encoder architectures categorized into three groups, aiming to differentiate their reconstruction ability.

Semi-supervised and unsupervised Generative Adversarial Networks. (GAN)-based methods have been gaining popularity in anomaly detection task recently.

The latent features of complex high-dimensional data can be well learned with autoencoder or GANs, and the learning ability directly determines the detection ...