Autoencoder Optimization for Anomaly Detection: A Comparative Study with Shallow Algorithms

This paper presents an innovative guide for optimizing autoencoder performance, specifically targeting anomaly detection tasks. In addressing prevalent issues in deep learning algorithms, our primary focus lies in effectively selecting and controlling the latent space in autoencoders. We comprehensively explore methodologies for determining the optimal latent size, a critical and often overlooked aspect in autoencoder architectures. This endeavor forms part of a broader initiative to enhance autoencoder efficacy, ensuring their performance is on par with or superior to many shallow learning algorithms, a challenge highlighted in studies like ADBENCH. Our approach encompasses a detailed examination and experimentation with various parameters, architectures, and loss functions, all aimed at refining the efficiency and accuracy of autoencoders in anomaly detection for image and tabular data. This research stands out for its dual focus on image and tabular datasets. We thoroughly examine the performance of autoencoders in detecting anomalies, utilizing a variety of autoencoder architectures and diverse hyperparameters.