To address this issue, we propose a novel hyperspectral anomaly detection method based on a spatial-spectral joint mask variational autoencoder (VAE). By combining the probabilistic modeling ...

Currently two models are supported, a simple Variational Autoencoder and a Disentangled version (beta-VAE). The model implementations can be found in the src/models directory. These models were ...

We present the Multi-Level Variational Autoencoder (ML-VAE), a new deep probabilistic model for learning a disentangled representation of grouped data. The ML-VAE separates the latent representation ...

In recent years, autoencoders and their variants have emerged as effective tools for hyperspectral anomaly detection. Nevertheless, owing to the complex distribution of anomalous regions and the ...

Currently two models are supported, a simple Variational Autoencoder and a Disentangled version (beta-VAE). The model implementations can be found in the src/models directory. These models were ...