Apr 26, 2022 · We propose Variational Auto-encoding Binary Classifiers (V-ABC): a novel model that repurposes and extends the Auto-encoding Binary Classifier ( ...
This paper presents an effective method for underwater target classification by the beta variational autoencoder model with Mel Frequency Cepstral ...
Apr 26, 2022 · We propose. Variational Auto-encoding Binary Classifiers (V-ABC): a novel model that repurposes and extends the Auto-encoding Binary. Classifier ...
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Jun 12, 2024 · Variational autoencoders (VAEs) are generative models used in machine learning to generate new data samples as variations of the input data ...
This paper proposes VLAD, a novel VAE-based Lifelong Anomaly Detection method addressing all these challenges simultaneously in complex task-agnostic scenarios.
Sep 9, 2024 · In this paper, we propose an unsupervised model-based anomaly detection named LVEAD, which assumpts that the anomalies are objects that do not ...
May 13, 2024 · We propose Frequency-enhanced Conditional Variational Autoencoder (FCVAE), a novel unsupervised AD method for univariate time series.
This paper proposes a novel approach to anomaly detection based on the Variational Autoencoder method with a Mish activation function and a Negative Log- ...
A CNN-VAE-based anomaly detection model and an LSTM network to generate temporal-aware embeddings of the latent vector of the primary model is used.
Nov 5, 2021 · This paper proposes a novel hybrid method for KPI anomaly detection based on VAE and support vector data description (SVDD).