Better Latent Spaces for Better Autoencoders

Abstract: Autoencoders as tools behind anomaly searches at the LHC have the structural problem that they only work in one direction, extracting jets with higher complexity but not the other way around. To address this, we derive classifiers from the latent space of (variational) autoencoders, specifically in Gaussian mixture and Dirichlet latent spaces. In particular, the Dirichlet setup solves the problem and improves both the performance and the interpretability of the networks.

• The paper introduces a novel approach by refining latent spaces with Gaussian mixture and Dirichlet distributions to boost autoencoder performance in anomaly detection.
• The paper derives classifiers directly from the latent space, streamlining the discrimination of complex data patterns in LHC jet classification.
• The paper demonstrates that using Dirichlet latent spaces enhances both performance and interpretability, which is critical for scientific applications.

The paper "Better Latent Spaces for Better Autoencoders" explores enhancing autoencoders, particularly for anomaly detection applications at the Large Hadron Collider (LHC). Traditional autoencoders face limitations in discriminating complex data patterns, often requiring adjustments to function effectively in this context.

Core Ideas

1. Latent Space Exploration: The authors propose that the latent space structure of autoencoders significantly impacts their performance. By adopting Gaussian mixture and Dirichlet distributions, they aim to refine how the latent space represents data patterns.
2. Classification from Latent Space: The study suggests deriving classifiers directly from the latent space. This approach could streamline the process of distinguishing between different types of data, especially useful in scenarios like jet classification at the LHC.
3. Dirichlet Latent Spaces: A key contribution of the paper is the introduction of Dirichlet latent spaces, which address the directional limitations of traditional autoencoders. This setup enhances performance by providing a more nuanced representation of data, facilitating improved anomaly detection.
4. Improved Interpretability: The use of Dirichlet distributions not only boosts performance but also enhances interpretability. This benefit is crucial in scientific applications where understanding the model's decision process is as important as the output itself.

Implications

This work has significant implications for using autoencoders in complex scientific and engineering problems. By improving both the effectiveness and interpretability of autoencoders, the paper provides a pathway for more reliable anomaly detection in particle physics experiments like those conducted at the LHC. This could lead to more accurate searches for new physics by better distinguishing between known processes and potential anomalies.