Learning Loss Functions for Semi-supervised Learning via Discriminative Adversarial Networks

Understand

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.