Conditioning Latent-Space Clusters for Real-World Anomaly Classification
Daniel Bogdoll Svetlana Pavlitska Simon Klaus J. Marius Zöllner
Anomalies in the domain of autonomous driving are a major hindrance to the
large-scale deployment of autonomous vehicles. In this work, we focus on
high-resolution camera data from urban scenes that include anomalies of various
types and sizes. Based on a Variational Autoencoder, we condition its latent
space to classify samples as either normal data or anomalies. In order to
emphasize especially small anomalies, we perform experiments where we provide
the VAE with a discrepancy map as an additional input, evaluating its impact on
the detection performance. Our method separates normal data and anomalies into
isolated clusters while still reconstructing high-quality images, leading to
meaningful latent representations.