2021/03/25 by Zhouzheng Li, Li, Zhouzheng, Kun Feng +1
Computer Science · #Anomaly Detection Techniques and Applications #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Machine Learning (cs.LG) #Neural Networks and Applications #cs.AI #cs.LG
paper · pdf · doi:10.48550/arxiv.2103.14082
openalex publication_date 2021/03/25 · arxiv created 2021/07/13 · arxiv updated 2021/07/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
While the beta-VAE family is aiming to find disentangled representations and acquire human-interpretable generative factors, like what an ICA (from the linear domain) does, we propose Full Encoder, a novel unified autoencoder framework as a correspondence to PCA in the non-linear domain. The idea is to train an autoencoder with one latent variable first, then involve more latent variables progressively to refine the reconstruction results. The Full Encoder is also a latent variable predictive model that the latent variables acquired are stable and robust, as they always learn the same representation regardless of the network initial states. Full Encoder can be used to determine the degrees of freedom in a simple non-linear system and can be useful for data compression or anomaly detection. Full Encoder can also be combined with the beta-VAE framework to sort out the importance of the generative factors, providing more insights for non-linear system analysis. These qualities will make FE useful for analyzing real-life industrial non-linear systems. To validate, we created a toy dataset with a custom-made non-linear system to test it and compare its properties to those of VAE and beta-VAE's.