Abstract: As a commonly used model for anomaly detection, the autoencoder model for anomaly detection does not train the objective for extracted features, which is a downside of autoencoder model. In ...

Abstract: Deep autoencoder (AE) has demonstrated promising performances in visual anomaly detection (VAD). Learning normal patterns on normal data, deep AE is expected to yield larger reconstruction ...

Research-grade hybrid malware detection system combining Random Forest, XGBoost, and a Deep Neural Network in a weighted ensemble, with an Autoencoder for zero-day anomaly detection — featuring SHAP ...

Attention-guided generator with dual discriminator GAN for real-time video anomaly detection 2024 J-EAAI Model Video anomaly detection guided by clustering learning 2024 J-PR Model Toward Video ...

In this thesis we propose a new form of Variational Autoencoder called the Conditional Latent Space Variational Autoencoder or CL-VAE. By conditioning on a known label in a dataset we can decide what ...

Abhinav Piratla, an AI security architect, is closing the critical gap in medical device protection. Discover how his ...