A Novel Variational Autoencoder with Applications to Generative Modelling, Classification, and Ordinal Regression

2018/12/18 by Joel Jaskari, Jaskari, Joel, Jyri Kivinen +1 · 1 citation
Computer Science · #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Generative Adversarial Networks and Image Synthesis #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Time Series Analysis and Forecasting

paper · pdf · doi:10.48550/arxiv.1812.07352

openalex publication_date 2018/12/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We develop a novel probabilistic generative model based on the variational autoencoder approach. Notable aspects of our architecture are: a novel way of specifying the latent variables prior, and the introduction of an ordinality enforcing unit. We describe how to do supervised, unsupervised and semi-supervised learning, and nominal and ordinal classification, with the model. We analyze generative properties of the approach, and the classification effectiveness under nominal and ordinal classification, using two benchmark datasets. Our results show that our model can achieve comparable results with relevant baselines in both of the classification tasks.

Citations

• Stochastic Backpropagation and Approximate Inference in Deep Generative Models
• Auto-Encoding Variational Bayes
• Auxiliary Deep Generative Models
• A simple squared-error reformulation for ordinal classification

Cited by

• Recursively Conditional Gaussian for Ordinal Unsupervised Domain Adaptation

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