用预训练GAN生成高分辨率稀有图像，兼顾多样性与真实性。
Diverse Rare Sample Generation with Pretrained GANs
- 通过梯度优化潜变量并结合归一化流估计特征空间密度
- 无需微调即可在多个数据集上生成多样且稀有的图像
- 可调节稀有度、多样性及与参考图的相似性，适合数据增强
深度生成模型在生成真实数据方面表现优异，但在低密度区域生成稀有样本时受限于训练数据稀缺和模式崩溃问题。现有方法虽提升生成质量，却常以牺牲多样性与覆盖率为代价，忽略稀有与新颖样本。本文提出一种基于预训练GAN的高分辨率图像稀有样本生成新方法，采用多目标框架下的潜变量梯度优化，并利用归一化流进行特征空间密度估计，实现对稀有度、多样性和参考图像相似性的可控生成。实验表明，该方法在不重新训练或微调预训练GAN的前提下，于多个数据集和GAN架构上均有效提升了稀有样本的生成质量与多样性，兼具定性与定量优势。
原文摘要 · Abstract (English)
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.
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