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A Case Study of Beta-Variational Auto-encoders, Disentanglement Impacts of Input Distribution and Beta-Variation Based Upon a Computational Multi-modal Particle Packing Simulation

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Abstract

We present work that quantifies the disentanglement of the reconstruction of beta-variational auto-encoders (\(\beta \)-VAEs) varying the hyper-parameter \(\beta \) for three different input distributions (Hall, https://zenodo.org/record/8003522, 2023). Currently the majority use of VAEs are for image processing and little work has been done in the field of material science using this machine learning technique to create reconstructions to explore the search for new designs. This work highlights the importance of the distribution shape can be more important than the quantity of data in creating neural network reconstructions such as \(\beta \)-VAEs which has been used for this effort. Furthermore, this work highlights that the best disentangled reconstruction doesn’t necessarily create the best reconstruction.

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Authors and Affiliations

  1. Northrop Grumman Corporation, Corinne, UT, USA

    Jason R. Hall

  2. Department of Materials Science and Engineering, University of Utah, Salt Lake City, UT, USA

    Jason R. Hall & Taylor D. Sparks

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  1. Jason R. Hall
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  2. Taylor D. Sparks
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Correspondence to Jason R. Hall.

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Hall, J.R., Sparks, T.D. A Case Study of Beta-Variational Auto-encoders, Disentanglement Impacts of Input Distribution and Beta-Variation Based Upon a Computational Multi-modal Particle Packing Simulation. Integr Mater Manuf Innov 12, 267–275 (2023). https://doi.org/10.1007/s40192-023-00306-6

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  • DOI: https://doi.org/10.1007/s40192-023-00306-6

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