<ul><li>Variational autoencoders (VAEs) often experience posterior collapse, leading to poor representation learning quality.</li><li>An adjustable hyperparameter beta has been introduced in VAEs to address posterior collapse.</li><li>This study examines the conditions under which posterior collapse occurs, as determined by beta and dataset size.</li><li>The findings reveal the inevitable posterior collapse beyond a certain beta threshold, regardless of dataset size, and the dataset size dependence of the rate-distortion curve in VAEs.</li></ul>

High-dimensional Asymptotics of VAEs: Threshold of Posterior Collapse and Dataset-Size Dependence of Rate-Distortion Curve

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