@article{Creswell:2016,
author = {Creswell, A and Bharath, AA},
title = {Task Specific Adversarial Cost Function},
url = {http://arxiv.org/abs/1609.08661v1},
year = {2016}
}

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TY  - JOUR
AB  - The cost function used to train a generative model should fit the purpose ofthe model. If the model is intended for tasks such as generating perceptuallycorrect samples, it is beneficial to maximise the likelihood of a sample drawnfrom 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 classificationit is beneficial to maximise the likelihood that a sample drawn from thetraining data is captured by the model, equivalent to minimising KL[P||Q]. Thecost function used in adversarial training optimises the Jensen-Shannon entropywhich 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 ofthe model for either task. Our task specific cost function is evaluated on adataset of hand-written characters in the following tasks: Generation,retrieval and one-shot learning.
AU  - Creswell,A
AU  - Bharath,AA
PY  - 2016///
TI  - Task Specific Adversarial Cost Function
UR  - http://arxiv.org/abs/1609.08661v1
ER  - 

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