A novel weight normalization technique to improve Generative Adversarial Network training
Keywords:
Weight Normalization, Generative Adversarial NetworksAbstract
Generative Adversarial Networks (GANs) have emerged as a groundbreaking framework for generating realistic data across various domains. Yet, their training still needs to be more manageable due to mode collapse and instability. This paper introduces a novel weight normalization technique designed to enhance the training process of GANs by improving convergence rates and overall model performance. Traditional approaches often rely on simple weight scaling or standard normalization methods that may not fully address the unique challenges posed by the adversarial training dynamic. Our proposed technique applies a more tailored normalization strategy that adapts to the evolving distribution of weights during training, ensuring more consistent gradient flow and better representational capacity. Through extensive experimentation, we demonstrate that our weight normalization approach significantly reduces the variance in generated samples, leading to higher fidelity outputs and a more stable training process. We also provide a comprehensive analysis of the impact of weight normalization on both the generator and discriminator networks, highlighting its effectiveness in mitigating common pitfalls associated with GAN training. Our findings suggest that integrating this novel technique enhances the quality of generated samples and facilitates a smoother training experience, making it easier for practitioners to deploy GANs in real-world applications. This work contributes to the ongoing efforts to refine GAN architectures and training methodologies, offering a promising avenue for further research in generative modeling. By presenting a fresh perspective on weight normalization, we aim to inspire subsequent advancements in the field, ultimately broadening the scope and applicability of GANs across various industries.
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