|Efficient Out-of-Distribution Detection Using Latent Space of β-VAE for Cyber-Physical Systems
Deep Neural Networks are actively being used in the design of autonomous Cyber-Physical Systems (CPSs). The advantage of these models is their ability to handle high-dimensional state-space and learn compact surrogate representations of the operational state spaces. However, the problem is that the sampled observations used for training the model may never cover the entire state space of the physical environment, and as a result, the system will likely operate in conditions that do not belong to the training distribution.
|Year of Publication
ACM Trans. Cyber-Phys. Syst.
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