Targeted collapse regularized autoencoder for anomaly detection: black hole at the center
arxiv(2023)
摘要
Autoencoders have been extensively used in the development of recent anomaly
detection techniques. The premise of their application is based on the notion
that after training the autoencoder on normal training data, anomalous inputs
will exhibit a significant reconstruction error. Consequently, this enables a
clear differentiation between normal and anomalous samples. In practice,
however, it is observed that autoencoders can generalize beyond the normal
class and achieve a small reconstruction error on some of the anomalous
samples. To improve the performance, various techniques propose additional
components and more sophisticated training procedures. In this work, we propose
a remarkably straightforward alternative: instead of adding neural network
components, involved computations, and cumbersome training, we complement the
reconstruction loss with a computationally light term that regulates the norm
of representations in the latent space. The simplicity of our approach
minimizes the requirement for hyperparameter tuning and customization for new
applications which, paired with its permissive data modality constraint,
enhances the potential for successful adoption across a broad range of
applications. We test the method on various visual and tabular benchmarks and
demonstrate that the technique matches and frequently outperforms more complex
alternatives. We further demonstrate that implementing this idea in the context
of state-of-the-art methods can further improve their performance. We also
provide a theoretical analysis and numerical simulations that help demonstrate
the underlying process that unfolds during training and how it helps with
anomaly detection. This mitigates the black-box nature of autoencoder-based
anomaly detection algorithms and offers an avenue for further investigation of
advantages, fail cases, and potential new directions.
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