We propose DIVERSE, a framework for systematically exploring the Rashomon set of deep neural networks, the collection of models that match a reference model's accuracy while differing in their predictive behavior. DIVERSE augments a pretrained model with Feature-wise Linear Modulation (FiLM) layers and uses Covariance Matrix Adaptation Evolution Strategy (CMA-ES) to search a latent modulation space, generating diverse model variants without retraining or gradient access. Across MNIST, PneumoniaMNIST, and CIFAR-10, DIVERSE uncovers multiple high-performing yet functionally distinct models. Our experiments show that DIVERSE offers a competitive and efficient exploration of the Rashomon set, making it feasible to construct diverse sets that maintain robustness and performance while supporting well-balanced model multiplicity. While retraining remains the baseline to generate Rashomon sets, DIVERSE achieves comparable diversity at reduced computational cost.

翻译：我们提出DIVERSE框架，用于系统性地探索深度神经网络的拉什蒙集合——即那些在保持参考模型准确性的同时具有不同预测行为的模型集合。DIVERSE通过特征线性调制层增强预训练模型，并采用协方差矩阵自适应进化策略在潜在调制空间中进行搜索，无需重新训练或梯度访问即可生成多样化的模型变体。在MNIST、PneumoniaMNIST和CIFAR-10数据集上的实验表明，DIVERSE能够发现多个高性能但功能各异的模型。我们的研究证明，DIVERSE为拉什蒙集合提供了高效且具有竞争力的探索方法，使得构建既保持鲁棒性和性能、又支持均衡模型多样性的集合成为可能。虽然重新训练仍是生成拉什蒙集合的基准方法，但DIVERSE能以更低的计算成本实现相当的多样性水平。