Data augmentation is one of the most prevalent tools in deep learning,
underpinning many recent advances, including those from classification,
generative models, and representation learning. The standard approach to data
augmentation combines simple transformations like rotations and flips to
generate new images from existing ones. However, these new images lack
diversity along key semantic axes present in the data. Consider the task of
recognizing different animals. Current augmentations fail to produce diversity
in task-relevant high-level semantic attributes like the species of the animal.
We address the lack of diversity in data augmentation with image-to-image
transformations parameterized by pre-trained text-to-image diffusion models.
Our method edits images to change their semantics using an off-the-shelf
diffusion model, and generalizes to novel visual concepts from a few labelled
examples. We evaluate our approach on image classification tasks in a few-shot
setting, and on a real-world weed recognition task, and observe an improvement
in accuracy in tested domains.

通过使用图像转图像的方法、以及预训练的文本到图像扩散模型将其参数化，解决了数据扩增过程中具有高级语义标注的动物种类等属性缺乏多样性的问题，并在少样本情境和杂草识别任务中得到实际应用。