  In this paper, we provide the first focused study on the discontinuities
(aka. holes) in the latent space of Variational Auto-Encoders (VAEs), a
phenomenon which has been shown to have a detrimental effect on model capacity.
When investigating latent holes, existing works are exclusively centred around
the encoder network and they merely explore the existence of holes. We tackle
these limitations by proposing a highly efficient Tree-based Decoder-Centric
(TDC) algorithm for latent hole identification, with a focal point on the text
domain. In contrast to past studies, our approach pays attention to the decoder
network, as a decoder has a direct impact on the model's output quality.
Furthermore, we provide, for the first time, in-depth empirical analysis of the
latent hole phenomenon, investigating several important aspects such as how the
holes impact VAE algorithms' performance on text generation, and how the holes
are distributed in the latent space.
