  Anomaly detection algorithms are typically applied to static, unchanging,
data features hand-crafted by the user. But how does a user systematically
craft good features for anomalies that have never been seen? Here we couple
deep learning with active learning -- in which an Oracle iteratively labels
small amounts of data selected algorithmically over a series of rounds -- to
automatically and dynamically improve the data features for efficient outlier
detection. This approach, AHUNT, shows excellent performance on MNIST, CIFAR10,
and Galaxy-DESI data, significantly outperforming both standard anomaly
detection and active learning algorithms with static feature spaces. Beyond
improved performance, AHUNT also allows the number of anomaly classes to grow
organically in response to Oracle's evaluations. Extensive ablation studies
explore the impact of Oracle question selection strategy and loss function on
performance. We illustrate how the dynamic anomaly class taxonomy represents
another step towards fully personalized rankings of different anomaly classes
that reflect a user's interests, allowing the algorithm to learn to ignore
statistically significant but uninteresting outliers (e.g., noise). This should
prove useful in the era of massive astronomical datasets serving diverse sets
of users who can only review a tiny subset of the incoming data.
