Multi-Population Co-Evolutionary Generative Adversarial Network Architecture Search for Zero-Shot Learning

What the paper says

Generative Adversarial Network (GAN) have become a dominant paradigm in Zero-Shot Learning (ZSL) for synthesizing features of unseen classes. However, the efficacy of these models relies heavily on their network architectures, the manual design of which is labor-intensive. While Neural Architecture Search (NAS) automates this process, existing approaches typically adopt a “divide-and-conquer” strategy that decouples the optimisation of the generator and discriminator to ensure stability. This separation neglects the intrinsic co-adaptive nature of adversarial training, often leading to mismatched architectures and sub-optimal performance. To address these limitations, this paper proposes a novel framework named Multi-Population Co-Evolutionary Generative Adversarial Network Architecture Search (MC-GANS). MC-GANS reformulates the search as a co-evolutionary task that evolves the generator and discriminator as a symbiotic system, integrating three key strategies: (1) a Wasserstein distance-based adaptive mechanism to dynamically balance adversarial training stability; (2) a diversity-guided multi-population strategy to prevent premature convergence; and (3) a channel attention mechanism is integrated into the search space to enhance feature selectivity. MC-GANS outperformed the state-of-the-art generative ZSL methods on two datasets and achieves competitive results on the other dataset. The maximum performance improvement in harmonic mean reaches up to 2.3%. Ablation studies and theoretical analysis further confirm the effectiveness of each component and the model’s ability to capture complex data distributions. Codes and models are available at https://github.com/Wang-Zhaoming/MC-GANS.