(1) The model tends to select more gain from a lower Kullback-Leibler (KL) divergence during training, which encourages the approximate posterior close to Gaussian prior, rendering the latent space of the former unused.
our work is supported by National Natural Science Foundation of China (61976154), Tianjin Natural Science Foundation (18JCY-BJC15500), National Natural Science Foundation of China (61771333), Tianjin Municipal Science and Technology Project (18ZXZNGX00330), and the Foundation of State Key Laboratory of Cognitive Intelligence, iFLYTEK(CIoS-20190001).