  Classical approaches for one-class problems such as one-class SVM and
isolation forest require careful feature engineering when applied to structured
domains like images. State-of-the-art methods aim to leverage deep learning to
learn appropriate features via two main approaches. The first approach based on
predicting transformations (Golan & El-Yaniv, 2018; Hendrycks et al., 2019a)
while successful in some domains, crucially depends on an appropriate
domain-specific set of transformations that are hard to obtain in general. The
second approach of minimizing a classical one-class loss on the learned final
layer representations, e.g., DeepSVDD (Ruff et al., 2018) suffers from the
fundamental drawback of representation collapse. In this work, we propose Deep
Robust One-Class Classification (DROCC) that is both applicable to most
standard domains without requiring any side-information and robust to
representation collapse. DROCC is based on the assumption that the points from
the class of interest lie on a well-sampled, locally linear low dimensional
manifold. Empirical evaluation demonstrates that DROCC is highly effective in
two different one-class problem settings and on a range of real-world datasets
across different domains: tabular data, images (CIFAR and ImageNet), audio, and
time-series, offering up to 20% increase in accuracy over the state-of-the-art
in anomaly detection. Code is available at https://github.com/microsoft/EdgeML.
