  Several deep models, esp. the generative, compare the samples from two
distributions (e.g. WAE like AutoEncoder models, set-processing deep networks,
etc) in their cost functions. Using all these methods one cannot train the
model directly taking small size (in extreme -- one element) batches, due to
the fact that samples are to be compared.
  We propose a generic approach to training such models using one-element
mini-batches. The idea is based on splitting the batch in latent into parts:
previous, i.e. historical, elements used for latent space distribution matching
and the current ones, used both for latent distribution computation and the
minimization process. Due to the smaller memory requirements, this allows to
train networks on higher resolution images then in the classical approach.
