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Modern robotic perception is highly dependent on neural networks.It is well known that neural network-based perception can be unreliable in real-world deployment,especially in difficult imaging conditions.Out-of-distribution detection is commonly proposed as a solution for ensuring reliability in real-world deployment.Previous work has shown that normalizing flow models can be used for out-of-dist
