Novelty detection is the machine learning task to recognize data belonging to an unknown pattern. Complementary to supervised learning, it allows to analyze data without a priori knowledge on signal or model-independently. In this talk, we would demonstrate the potential role of novelty detection in collider physics, using autoencoder-based deep neural network. Explicitly, we develop a set of density-based novelty evaluators, which are sensitive to the clustering of unknown-pattern data/signal events, for optimizing detection algorithms. We also study the generic influence of the known-pattern data fluctuations on detection sensitivity which arise from non-signal regions in the feature space (Look Elsewhere Effect). Strategies to address it are proposed. For proof of concept, the algorithms are applied to detecting signal/novel events which are defined by fermionic di-top partner and resonant di-top productions at LHC, and by exotic Higgs decays of two specific modes at future e+e- collider, respectively. With parton-level analysis, we show that the signal of new-physics benchmarks could be recognized with high efficiency.