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.Comment: 11 pages 12 figure