  Anomaly detection techniques are growing in importance at the Large Hadron
Collider (LHC), motivated by the increasing need to search for new physics in a
model-agnostic way. In this work, we provide a detailed comparative study
between a well-studied unsupervised method called the autoencoder (AE) and a
weakly-supervised approach based on the Classification Without Labels (CWoLa)
technique. We examine the ability of the two methods to identify a new physics
signal at different cross sections in a fully hadronic resonance search. By
construction, the AE classification performance is independent of the amount of
injected signal. In contrast, the CWoLa performance improves with increasing
signal abundance. When integrating these approaches with a complete background
estimate, we find that the two methods have complementary sensitivity. In
particular, CWoLa is effective at finding diverse and moderately rare signals
while the AE can provide sensitivity to very rare signals, but only with
certain topologies. We therefore demonstrate that both techniques are
complementary and can be used together for anomaly detection at the LHC.
