  The cost function used to train a generative model should fit the purpose of
the model. If the model is intended for tasks such as generating perceptually
correct samples, it is beneficial to maximise the likelihood of a sample drawn
from the model, Q, coming from the same distribution as the training data, P.
This is equivalent to minimising the Kullback-Leibler (KL) distance, KL[Q||P].
However, if the model is intended for tasks such as retrieval or classification
it is beneficial to maximise the likelihood that a sample drawn from the
training data is captured by the model, equivalent to minimising KL[P||Q]. The
cost function used in adversarial training optimises the Jensen-Shannon entropy
which can be seen as an even interpolation between KL[Q||P] and KL[P||Q]. Here,
we propose an alternative adversarial cost function which allows easy tuning of
the model for either task. Our task specific cost function is evaluated on a
dataset of hand-written characters in the following tasks: Generation,
retrieval and one-shot learning.
