Constructed by the neural network, variational autoencoder has the overfitting problem caused by setting too many neural units, we develop an adaptive dimension reduction algorithm that can automatically learn the dimension of latent variable vector, moreover, the dimension of every hidden layer. This approach not only apply to the variational autoencoder but also other variants like Conditional VAE(CVAE), and we show the empirical results on six data sets which presents the universality and efficiency of this algorithm. The key advantages of this algorithm is that it can converge the dimension of latent variable vector which approximates the dimension reaches minimum loss of variational autoencoder(VAE), also speeds up the generating and computing speed by reducing the neural units.

翻译：由神经网络构造的变异自动编码器由于设置太多神经元而造成超适应问题,我们开发了适应性维度减少算法,可以自动了解潜伏变量矢量的维度,此外,每个隐藏层的维度。这种方法不仅适用于变异自动编码器,也适用于其他变异器,如条件VAE(CVAE),我们展示了六个数据集的经验结果,这些数据集体现了这种算法的普遍性和效率。这种算法的主要优点是,它能够将潜伏变量矢量的维度趋近于该维量的维度,从而通过减少神经元来加速生成和计算速度。