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Found 3 papers, 1 papers with code
Improving Intrusion Detection with Domain-Invariant Representation Learning in Latent Space
Domain generalization focuses on leveraging knowledge from multiple related domains with ample training data and labels to enhance inference on unseen in-distribution (IN) and out-of-distribution (OOD) domains.
A Latent Space Correlation-Aware Autoencoder for Anomaly Detection in Skewed Data
The multi-objective function has two goals -- it measures correlation information in the latent feature space in the form of robust MD distance and simultaneously tries to preserve useful correlation information from the original data space in the latent space by maximizing mutual information between the prior and latent space.
Deep diffusion-based forecasting of COVID-19 by incorporating network-level mobility information
The Gaussian Mixture Model layer is implemented to consider the multimodal nature of the real-time data while learning from multiple related time series.
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