Population-Based Multi-Objective Training of Discriminators for Semi-Supervised GANs

• URL: http://arxiv.org/abs/2607.01907v1
• Date: Thu, 02 Jul 2026 09:06:06 GMT
• Title: Population-Based Multi-Objective Training of Discriminators for Semi-Supervised GANs
• Abstract summary: Semi-supervised generative adversarial networks (SSL-GANs) can exploit large unlabeled datasets while retaining a classifier in the discriminator.<n>This paper proposes a population-based evolutionary training strategy in which discriminator learning is formulated as a multi-objective optimization problem.
• Score: 0.3313576045747071
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Semi-supervised generative adversarial networks (SSL-GANs) can exploit large unlabeled datasets while retaining a classifier in the discriminator, but their training is often unstable. This paper proposes a population-based evolutionary training strategy in which discriminator learning is formulated as a multi-objective optimization problem. Instead of aggregating the supervised and unsupervised components of the SSL objective into a single scalar loss, the method maintains a population of discriminators ranked by Pareto dominance, enabling the exploration of different trade-offs between classification accuracy and real/fake discrimination. This formulation aims to improve both roles of SSL-GANs: learning accurate classifiers and training generators capable of producing realistic samples. We analyze several variants, including an elitist strategy and a mono-objective ablation, to assess the role of multi-objective selection. Experiments on MNIST with limited labels show improved training robustness compared to SSL-GAN and CE-SSL-GAN state-of-the-art baselines, while the elitist variant consistently achieves the highest classification accuracy.

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