  In this draft, which reports on work in progress, we 1) adapt the information
bottleneck functional by replacing the compression term by class-conditional
compression, 2) relax this functional using a variational bound related to
class-conditional disentanglement, 3) consider this functional as a training
objective for stochastic neural networks, and 4) show that the latent
representations are learned such that they can be used in a naive Bayes
classifier. We continue by suggesting a series of experiments along the lines
of Nonlinear In-formation Bottleneck [Kolchinsky et al., 2018], Deep
Variational Information Bottleneck [Alemi et al., 2017], and Information
Dropout [Achille and Soatto, 2018]. We furthermore suggest a neural network
where the decoder architecture is a parameterized naive Bayes decoder.
