  Out-of-distribution (OOD) detection aims at enhancing standard deep neural
networks to distinguish anomalous inputs from original training data. Previous
progress has introduced various approaches where the in-distribution training
data and even several OOD examples are prerequisites. However, due to privacy
and security, auxiliary data tends to be impractical in a real-world scenario.
In this paper, we propose a data-free method without training on natural data,
called Class-Conditional Impressions Reappearing (C2IR), which utilizes image
impressions from the fixed model to recover class-conditional feature
statistics. Based on that, we introduce Integral Probability Metrics to
estimate layer-wise class-conditional deviations and obtain layer weights by
Measuring Gradient-based Importance (MGI). The experiments verify the
effectiveness of our method and indicate that C2IR outperforms other post-hoc
methods and reaches comparable performance to the full access (ID and OOD)
detection method, especially in the far-OOD dataset (SVHN).
