Variational Autoencoders (VAEs) are powerful generative models that have been
widely used in various fields, including image and text generation. However,
one of the known challenges in using VAEs is the model's sensitivity to its
hyperparameters, such as the latent space size. This paper presents a simple
extension of VAEs for automatically determining the optimal latent space size
during the training process by gradually decreasing the latent size through
neuron removal and observing the model performance. The proposed method is
compared to traditional hyperparameter grid search and is shown to be
significantly faster while still achieving the best optimal dimensionality on
four image datasets. Furthermore, we show that the final performance of our
method is comparable to training on the optimal latent size from scratch, and
might thus serve as a convenient substitute.

这篇文章介绍了一种对变分自动编码器 (VAEs) 进行简单扩展的方法，通过渐进性减小潜空间大小来自动确定训练过程中的最佳潜空间大小，并将该方法与传统的超参数网格搜索进行比较，结果表明其速度显著更快，且在四个图像数据集上实现了最佳的维度。此外，还证明了我们方法的最终性能与从头开始训练的最佳潜空间大小相当，因此可能作为一种便利的替代方法。