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December 2019

tl;dr: A new benchmark measuring how well methods detect potentially hazardous anomalies in driving scenes.

Overall impression

Embeddings of intermediate layers hold important information for anomaly detection.

Key ideas

  • Bayesian DL: epistemic uncertainty, aleatoric uncertainty, distributional uncertainty
  • Novelty detection (Out of distribution detection): one class cls which aim at discriminative embeddings, density estimations, and generative reconstruction.
  • Softmax score is not a reliable score for anomaly detection
  • Most better performing methods require special loss that reduced segmentation accuracy (tradeoff between better outlier detection and error. Cf tradeoff between better uncertainty calib and error)
  • Learning anomaly detection from fixed OoD data is on par with unsupervised methods for most of the datasets. Void classifier is most practical way forward. A separate void class is concisely better than maximizing the softmax entropy. A separate void class is also most practical.

Technical details

Notes