Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Efficient Out-of-Distribution Detection Using Latent Space of
  $\beta$-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.
These conditions that do not belong to training distribution are referred to as
Out-of-Distribution (OOD). Detecting OOD conditions at runtime is critical for
the safety of CPS. In addition, it is also desirable to identify the context or
the feature(s) that are the source of OOD to select an appropriate control
action to mitigate the consequences that may arise because of the OOD
condition. In this paper, we study this problem as a multi-labeled time series
OOD detection problem over images, where the OOD is defined both sequentially
across short time windows (change points) as well as across the training data
distribution. A common approach to solving this problem is the use of
multi-chained one-class classifiers. However, this approach is expensive for
CPSs that have limited computational resources and require short inference
times. Our contribution is an approach to design and train a single
$\beta$-Variational Autoencoder detector with a partially disentangled latent
space sensitive to variations in image features. We use the feature sensitive
latent variables in the latent space to detect OOD images and identify the most
likely feature(s) responsible for the OOD. We demonstrate our approach using an
Autonomous Vehicle in the CARLA simulator and a real-world automotive dataset
called nuImages.

34
The categories of this paper are: chem-ph
31
The categories of this paper are: ao-sci
12
The categories of this paper are: nucl-th
19
The categories of this paper are: eess
29
The categories of this paper are: funct-an
9
The categories of this paper are: nlin
7
The categories of this paper are: hep-th
17
The categories of this paper are: q-fin
6
The categories of this paper are: astro-ph
18
The categories of this paper are: econ
10
The categories of this paper are: q-bio
24
The categories of this paper are: dg-ga
5
The categories of this paper are: gr-qc
8
The categories of this paper are: hep-ex
15
The categories of this paper are: nucl-ex
3
The categories of this paper are: physics