Exploring the i3DVAE-LSTM Framework for Generating Exceptionally Rare Anomaly Signals

Abstract

In the era of data-driven approaches, ensuring data quality is crucial for developing effective machine learning and deep learning models. While data augmentation is commonly used to increase the sample size, it does not guarantee data quality. Data generation goes beyond augmentation by incorporating additional steps to ensure high-quality output samples. This technique is particularly valuable for anomaly classification tasks with limited training samples. A recent study introduced a 3DVAE-LSTM (3-Dimensional Variational Autoencoders-Long Short-Term Memory) approach for generating extremely rare case signals. Although this framework synthesized samples for training deep learning models, it faced challenges with long sequential data. To address this, the authors proposed an improved version called i3DVAE-LSTM (Improvement of 3-Dimensional Variational Autoencoders-Long Short-Term Memory) and presented the evaluation of the i3DVAE-LSTM framework. The proposed framework adopts a divide-and-conquer technique, splitting long sequence data into smaller fragments to enhance the quality of generated samples, which are then concatenated together. Experimental results demonstrated that classification models trained with data generated by i3DVAE-LSTM outperformed baselines in all aspects.