# Task Specific Adversarial Cost Function

Antonia Creswell, Anil A. Bharath

(Submitted on 27 Sep 2016)

Abstract:

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.