Conceitos Básicos
The key to improving autoencoders in anomaly detection lies in minimizing the information entropy of latent vectors.
Resumo
Autoencoders are widely used in medical anomaly detection, operating on the assumption that they can effectively reconstruct normal regions but struggle with unseen abnormal regions. Various methods have been proposed to enhance reconstruction quality and prevent the reconstruction of abnormal regions. However, these methods lack a solid theoretical foundation, leading to suboptimal solutions. This study provides a theoretical framework for autoencoder-based anomaly detection, emphasizing the importance of minimizing the entropy of latent vectors. Experiments on different datasets validate the effectiveness of this approach, showcasing significant performance improvements by reducing latent dimensions. The findings suggest that adjusting latent dimensions based on information theory principles can lead to more reliable anomaly detection systems.
Estatísticas
Reconstruction errors w.r.t. the latent dimension on RSNA dataset.
Performance of AE with different values of latent dimension d on multiple datasets.
Citações
"We prove that an appropriate latent dimension can avoid 'identical shortcut' in AE."
"Our theory suggests that in AD, AE tends to benefit from minimizing the entropy of the latent space."