Latent Regularization in Generative Test Input Generation

2026-02-17

AI Summary

This work addresses the limited effectiveness and diversity of generative test inputs in boundary testing of deep learning image classifiers by proposing a latent-space truncation regularization method based on StyleGAN. By integrating latent code mixing with a bisection-search-optimized truncation strategy, the approach efficiently generates high-quality and diverse boundary test samples in the latent space. Experimental results on MNIST, Fashion-MNIST, and CIFAR-10 demonstrate that the proposed method significantly outperforms random truncation, achieving higher fault detection rates while simultaneously enhancing both the validity and diversity of the generated test inputs.

Technology Category

• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Generation

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

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.

Problem

• latent regularization
• test input generation
• GANs
• fault detection
• boundary testing

Innovation

• latent regularization
• truncation
• generative test input
• fault detection
• style-based GANs