  We leverage representation learning and the inductive bias in
neural-net-based Standard Model jet classification tasks, to detect non-QCD
signal jets. In establishing the framework for classification-based anomaly
detection in jet physics, we demonstrate that, with a \emph{well-calibrated}
and \emph{powerful enough feature extractor}, a well-trained
\emph{mass-decorrelated} supervised Standard Model neural jet classifier can
serve as a strong generic anti-QCD jet tagger for effectively reducing the QCD
background. Imposing \emph{data-augmented} mass-invariance (and thus decoupling
the dominant factor) not only facilitates background estimation, but also
induces more substructure-aware representation learning. We are able to reach
excellent tagging efficiencies for all the test signals considered. In the best
case, we reach a background rejection rate of 51 and a significance improvement
factor of 3.6 at 50 \% signal acceptance, with the jet mass decorrelated. This
study indicates that supervised Standard Model jet classifiers have great
potential in general new physics searches.
