  Periodic variables illuminate the physical processes of stars throughout
their lifetime. Wide-field surveys continue to increase our discovery rates of
periodic variable stars. Automated approaches are essential to identify
interesting periodic variable stars for multi-wavelength and spectroscopic
follow-up. Here, we present a novel unsupervised machine learning approach to
hunt for anomalous periodic variables using phase-folded light curves presented
in the Zwicky Transient Facility Catalogue of Periodic Variable Stars by
\citet{Chen_2020}. We use a convolutional variational autoencoder to learn a
low dimensional latent representation, and we search for anomalies within this
latent dimension via an isolation forest. We identify anomalies with irregular
variability. Most of the top anomalies are likely highly variable Red Giants or
Asymptotic Giant Branch stars concentrated in the Milky Way galactic disk; a
fraction of the identified anomalies are more consistent with Young Stellar
Objects. Detailed spectroscopic follow-up observations are encouraged to reveal
the nature of these anomalies.
