This study investigates the impact of regularization of latent spaces through truncation on the quality of generated test inputs for deep learning classifiers. We evaluate this effect using style-based GANs, a state-of-the-art generative approach, and assess quality along three dimensions: validity, diversity, and fault detection. We evaluate our approach on the boundary testing of deep learning image classifiers across three datasets, MNIST, Fashion MNIST, and CIFAR-10. We compare two truncation strategies: latent code mixing with binary search optimization and random latent truncation for generative exploration. Our experiments show that the latent code-mixing approach yields a higher fault detection rate than random truncation, while also improving both diversity and validity.

翻译：本研究探讨了通过截断对潜在空间进行正则化对深度学习分类器生成测试输入质量的影响。我们采用基于风格的生成对抗网络（一种先进的生成方法）评估该效果，并从三个维度评估生成质量：有效性、多样性和故障检测能力。我们在三个数据集（MNIST、Fashion MNIST和CIFAR-10）上对深度学习图像分类器进行边界测试来评估所提方法。我们比较了两种截断策略：采用二分搜索优化的潜在编码混合方法，以及用于生成探索的随机潜在截断方法。实验结果表明，潜在编码混合方法相比随机截断具有更高的故障检测率，同时还能提升生成样本的多样性和有效性。