Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Loss Functions for Semi-supervised Learning via Discriminative
  Adversarial Networks   We propose discriminative adversarial networks (DAN) for semi-supervised
learning and loss function learning. Our DAN approach builds upon generative
adversarial networks (GANs) and conditional GANs but includes the key
differentiator of using two discriminators instead of a generator and a
discriminator. DAN can be seen as a framework to learn loss functions for
predictors that also implements semi-supervised learning in a straightforward
manner. We propose instantiations of DAN for two different prediction tasks:
classification and ranking. Our experimental results on three datasets of
different tasks demonstrate that DAN is a promising framework for both
semi-supervised learning and learning loss functions for predictors. For all
tasks, the semi-supervised capability of DAN can significantly boost the
predictor performance for small labeled sets with minor architecture changes
across tasks. Moreover, the loss functions automatically learned by DANs are
very competitive and usually outperform the standard pairwise and negative
log-likelihood loss functions for both semi-supervised and supervised learning.

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The categories of this paper are: astro-ph
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The categories of this paper are: comp-gas
2
The categories of this paper are: math
10
The categories of this paper are: q-bio
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The categories of this paper are: eess
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The categories of this paper are: quant-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: q-fin
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The categories of this paper are: adap-org
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The categories of this paper are: mtrl-th
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The categories of this paper are: nlin
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The categories of this paper are: hep-ex
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The categories of this paper are: dg-ga
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The categories of this paper are: acc-phys
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The categories of this paper are: nucl-th