  Reliable out-of-distribution (OOD) detection is fundamental to implementing
safer modern machine learning (ML) systems. In this paper, we introduce Igeood,
an effective method for detecting OOD samples. Igeood applies to any
pre-trained neural network, works under various degrees of access to the ML
model, does not require OOD samples or assumptions on the OOD data but can also
benefit (if available) from OOD samples. By building on the geodesic
(Fisher-Rao) distance between the underlying data distributions, our
discriminator can combine confidence scores from the logits outputs and the
learned features of a deep neural network. Empirically, we show that Igeood
outperforms competing state-of-the-art methods on a variety of network
architectures and datasets.
