Efficient Multi-Class Out-of-Distribution Reasoning for Perception Based Networks: Work-in-Progress
Perception-based deep neural networks used in Cyber Physical Systems are known to fail when faced with inputs that are out-of-distribution (OOD). OOD detection is a complex problem as we need to first identify the shift in the test data from the training distribution and then we need to isolate the responsible generative factor(s) (weather, lighting levels, traffic density, etc.). Unlike the state of the art that uses multi-chained one-class classifiers, we propose an efficient single monitor that uses the principle of disentanglement to train the latent space of a variational autoencoder to be sensitive to distribution shifts in different generative factors. We demonstrate our approach using an end-to-end driving controller in the CARLA simulator.