  Advancements in deep learning techniques have given a boost to the
performance of anomaly detection. However, real-world and safety-critical
applications demand a level of transparency and reasoning beyond accuracy. The
task of anomaly detection (AD) focuses on finding whether a given sample
follows the learned distribution. Existing methods lack the ability to reason
with clear explanations for their outcomes. Hence to overcome this challenge,
we propose Transparent {A}nomaly Detection {C}oncept {E}xplanations (ACE). ACE
is able to provide human interpretable explanations in the form of concepts
along with anomaly prediction. To the best of our knowledge, this is the first
paper that proposes interpretable by-design anomaly detection. In addition to
promoting transparency in AD, it allows for effective human-model interaction.
Our proposed model shows either higher or comparable results to black-box
uninterpretable models. We validate the performance of ACE across three
realistic datasets - bird classification on CUB-200-2011, challenging
histopathology slide image classification on TIL-WSI-TCGA, and gender
classification on CelebA. We further demonstrate that our concept learning
paradigm can be seamlessly integrated with other classification-based AD
methods.
