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

arXiv: https://arxiv.org/abs/1707.02198 · HTML: https://mlsanity.com/paper/1707.02198/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jul 2017.

## Built on by (most cited first, 3 of 3)

- [Stateless actor-critic for instance segmentation with high-level priors](https://mlsanity.com/paper/2107.02600/) (2021)
- [Depth and Feature Learning are Provably Beneficial for Neural Network Discriminators](https://mlsanity.com/paper/2112.13867/) (2021)
- [Cumulative Meta-Learning from Active Learning Queries for Robustness to Spurious Correlations](https://mlsanity.com/paper/2605.20771/) (2026)

## Abstract (arXiv)

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.