  We present DarkCLR, a novel framework for detecting semi-visible jets at the
LHC. DarkCLR uses a self-supervised contrastive-learning approach to create
observables that are approximately invariant under relevant transformations. We
use background-enhanced data to create a sensitive representation and evaluate
the representations using a normalized autoencoder as a density estimator. Our
results show a remarkable sensitivity for a wide range of semi-visible jets and
are more robust than a supervised classifier trained on a specific signal.
