一句话：提出 DESIGNATOR 工具集，集成火星环境模拟器与 GAN 增强搜索式测试，为火星视觉 DNN 生成测试与重训练数据集。
本文介绍 DESIGNATOR，一个用于在类火星环境中测试和重训练执行计算机视觉任务的深度神经网络（DNN）的数据集生成工具集。它集成火星环境模拟器 Marsim 与搜索式方法 DESIGNATE，后者结合仿真、生成对抗网络（GAN）和基于搜索的测试输入生成。用户可选择搜索策略、在 MarsSim 中启动仿真，并观察模拟图像、对应真实感图像、真值标签、模型预测及适应度值的演化。该工具帮助研究者与实践者生成能暴露失败并用于重训练 DNN 的数据集，也可作为解释元启发式搜索生成图像数据集的教学工具。MarsSim 还提供独立 API 和 GUI，支持在汽车领域之外评估搜索式方法。
We present DESIGNATOR, a toolset for generating datasets for testing and retraining deep neural networks (DNNs) performing computer vision tasks in Martian-like environments. The toolset integrates Marsim, a simulator of the Mars environment, and DESIGNATE, a search-based approach combining simulation, generative adversarial networks (GANs), and search-based test input generation. The tool enables users to select a search strategy, launch simulations in MarsSim, and observe the evolution of simulated images, corresponding realistic images, ground truth labels, model predictions, and fitness values. Beyond supporting researchers and practitioners in generating datasets capable of identifying failures and retraining DNNs, DESIGNATOR can be used as a didactic tool to explain how image datasets can be generated using meta-heuristic search. Furthermore, MarsSim can be used standalone, through its API and GUI. MarsSim enables researchers to assess search-based approaches beyond the predominantly studied automotive context. A demo video of DESIGNATOR is available at https: //youtu.be/fGxgpgViPEo.
@inproceedings{paperbot9534,
title = {DESIGNATOR: a Toolset for Automated GAN-enhanced Search-based Testing and Retraining of DNNs in Martian Environments},
author = {Mohammed Oualid Attaoui and Fabrizio Pastore},
booktitle = {ASE},
year = {2025},
doi = {10.1109/ase63991.2025.00355}
}