  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. Current augmentations
cannot alter the high-level semantic attributes, such as animal species present
in a scene, to enhance the diversity of data. 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
few-shot image classification tasks, and on a real-world weed recognition task,
and observe an improvement in accuracy in tested domains.
