2020
Learning Disentangled Representations of Videos with Missing Data
NeurIPS 2020poster
Missing data poses significant challenges while learning representations of video sequences. We present Disentangled Imputed Video autoEncoder (DIVE), a deep generative model that imputes and predicts future video frames in the presence of missing data. Specifically, DIVE introduces a missingness la…