This paper presents LatentPatch, a new method for generating realistic images from a small dataset of only a few images. We use a lightweight model with only a few thousand parameters. Unlike traditional few-shot generation methods that finetune pre-trained large-scale generative models, our approach is computed directly on the latent distribution by sequential feature matching, and is explainable by design. Avoiding large models based on transformers, recursive networks, or self-attention, which are not suitable for small datasets, our method is inspired by non-parametric texture synthesis and style transfer models, and ensures that generated image features are sampled from the source distribution. We extend previous single-image models to work with a few images and demonstrate that our method can generate realistic images, as well as enable conditional sampling and image editing. We conduct experiments on face datasets and show that our simplistic model is effective and versatile.

翻译：本文提出潜伏补丁（LatentPatch），一种仅需少量图像构成的小数据集即可生成逼真图像的新方法。我们采用参数量仅数千的轻量级模型。与依赖微调预训练大规模生成模型的前沿小样本生成方法不同，本方法通过顺序特征匹配直接在潜在分布上进行计算，且具备可解释性设计。针对不适用于小数据集的基于Transformer、递归网络或自注意力机制的大型模型，本方法受非参数纹理合成与风格迁移模型的启发，确保生成图像特征从源分布中采样。我们将原有单图像模型扩展至多图像场景，证明该方法既能生成逼真图像，亦可实现条件采样与图像编辑。基于人脸数据集的实验表明，该简约模型兼具高效性与通用性。