Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Task Specific Adversarial Cost Function   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.

27
The categories of this paper are: comp-gas
3
The categories of this paper are: physics
7
The categories of this paper are: hep-th
17
The categories of this paper are: q-fin
26
The categories of this paper are: bayes-an
2
The categories of this paper are: math
8
The categories of this paper are: hep-ex
6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
35
The categories of this paper are: mtrl-th
11
The categories of this paper are: hep-lat
5
The categories of this paper are: gr-qc
37
The categories of this paper are: supr-con
25
The categories of this paper are: patt-sol
24
The categories of this paper are: dg-ga
13
The categories of this paper are: quant-ph