Deep generative models are proficient in generating realistic data but struggle with producing rare samples in low density regions due to their scarcity of training datasets and the mode collapse problem. While recent methods aim to improve the fidelity of generated samples, they often reduce diversity and coverage by ignoring rare and novel samples. This study proposes a novel approach for generating diverse rare samples from high-resolution image datasets with pretrained GANs. Our method employs gradient-based optimization of latent vectors within a multi-objective framework and utilizes normalizing flows for density estimation on the feature space. This enables the generation of diverse rare images, with controllable parameters for rarity, diversity, and similarity to a reference image. We demonstrate the effectiveness of our approach both qualitatively and quantitatively across various datasets and GANs without retraining or fine-tuning the pretrained GANs.

翻译：深度生成模型擅长生成逼真数据，但由于训练数据稀缺和模式坍塌问题，难以在低密度区域生成稀有样本。现有方法虽致力于提升生成样本的保真度，却常因忽略稀有及新颖样本而导致多样性与覆盖度下降。本研究提出一种基于预训练GAN从高分辨率图像数据集中生成多样化稀有样本的新方法。该方法采用多目标框架下的潜在向量梯度优化，并利用标准化流进行特征空间密度估计，从而能够生成具有可控稀有度、多样性及参考图像相似度参数的多样化稀有图像。我们在多种数据集和GAN模型上通过定性与定量实验验证了本方法的有效性，且无需对预训练GAN进行再训练或微调。