arXiv:2607.01907v1 Announce Type: new 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 e
The paper addresses the persistent challenge of instability in semi-supervised GAN training, a key hurdle for leveraging vast unlabeled datasets in AI development.
Improved stability and efficiency in GAN training can accelerate advancements in generative AI, impacting capabilities from data synthesis to content creation across various applications.
This evolutionary approach to discriminator training could lead to more robust and higher-quality generative models, requiring less manual fine-tuning and potentially broader adoption.
- · AI researchers
- · Generative AI startups
- · Data-intensive industries
- · Cloud computing providers
- · Companies reliant on solely supervised learning
- · Inefficient GAN training methods
More stable and performant semi-supervised GANs become accessible for a wider range of applications.
This could lead to a proliferation of synthetic data generation for training other AI models, reducing reliance on expensive hand-labeled datasets.
Enhanced generative capabilities might accelerate the development of autonomous AI systems with more nuanced understanding of complex data distributions.
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Read at arXiv cs.LG