Presenter(s)

Fangshi Zhou

Comments

Presentation: 11:20-11:40 p.m., Jessie Hathcock Hall 180

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Description

Recently variational autoencoders (VAE) have become one of the most popular generative models in deep learning. It can be applied to generate images, audio, text, and other data. We propose a novel parallel structure for Gumbel-Softmax VAEs, which combines m ≥ 1 parallel VAEs with different annealing mechanics for softmax temperature τ and adjusts τ at each training epoch based on the minimum loss from these VAEs. Our preliminary experiments demonstrate that our model with m > 1 (e.g., m = 5) outperforms the model with m = 1 in generative processes, adversarial robustness, and denoising.

Publication Date

4-19-2023

Project Designation

Graduate Research

Primary Advisor

Zhongmei Yao, Xin Chen, Luan Nguyen, Tianming Zhao

Primary Advisor's Department

Computer Science

Keywords

Stander Symposium, College of Arts and Sciences

Institutional Learning Goals

Vocation

MinLoss-VAE: Min-Loss Parallel Variational Autoencoders with Categorical Latent Space

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