UT at Chattanooga Assistant Professor is leading a research project that has achieved a significant breakthrough
The study introduced a new computational framework called the Langevin Variational Autoencoder.
University of Tennessee at Chattanooga (UTC) Assistant Professor Zihao Wang is leading a research collaboration that has achieved a significant breakthrough in interpretable 3D image modeling.
Wang, who joined the UTC Department of Computer Science and Engineering faculty in 2024, partnered with researchers from the French Institute for Research in Computer Science and Automation to develop a lightweight artificial intelligence model capable of learning to disentangle shape and appearance in images.
Their paper, “Multi-energy Quasi-Symplectic Langevin Inference for Latent Disentangled Learning,” was recently accepted by IEEE Transactions on Image Processing—a monthly peer-reviewed journal published by the Institute of Electrical and Electronics Engineers. The journal is considered one of the field’s most respected sources for research in image and signal processing.
Wang and his collaborators tackled a long-standing challenge in 3D image modeling: how to build models that are lightweight, interpretable, and still deliver high-quality generative performance. Traditional deep learning approaches often achieve only two of those goals at once, he said.
The study introduced a new computational framework called the Langevin Variational Autoencoder (Langevin-VAE), which helps computers better understand the difference between an object’s shape and its surface details in medical images.
By using a quasi-symplectic integrator—a method that simplifies complex calculations—the model “avoids the expensive matrix calculations that typically slow down inference in high-dimensional data.”
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