Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images

  title={Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images},
  author={Philipp Liznerski and Lukas Ruff and Robert A. Vandermeulen and Billy Joe Franks and Klaus-Robert Muller and Marius Kloft},
Traditionally anomaly detection (AD) is treated as an unsupervised problem utilizing only normal samples due to the intractability of characterizing everything that looks unlike the normal data. However, it has recently been found that unsupervised image anomaly detection can be drastically improved through the utilization of huge corpora of random images to represent anomalousness; a technique which is known as Outlier Exposure . In this paper we show that specialized AD learning methods seem… 


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