Improving Diffusion-based Data Augmentation with Inversion Spherical Interpolation
<|reference_start|>Improving Diffusion-based Data Augmentation with Inversion Spherical Interpolation: Data Augmentation (DA), \ie, synthesizing faithful and diverse samples to expand the original training set, is a prevalent and effective strategy to improve various visual recognition tasks. With the powerful image generation ability, diffusion-based DA has shown strong performance gains on different benchmarks. In this paper, we analyze today's diffusion-based DA methods, and argue that they cannot take account of both faithfulness and diversity, which are two critical keys for generating high-quality samples and boosting final classification performance. To this end, we propose a novel Diffusion-based Inversion Interpolation DA method: Diff-II. Specifically, Diff-II consists of three main steps: 1) Category concepts learning: Learning concept embeddings for each category. 2) Inversion interpolation: Calculating the inversion for each image, and conducting spherical interpolation for two randomly sampled inversions from the same category. 3) Two-stage denoising: Using different prompts to generate synthesized images in a coarse-to-fine manner. Extensive experiments on multiple image classification tasks (\eg, few-shot, long-tailed, and out-of-distribution classification) have demonstrated its effectiveness over state-of-the-art diffusion-based DA methods.<|reference_end|>
@article{wang2024improving,
    title={Improving Diffusion-based Data Augmentation with Inversion Spherical
  Interpolation},
    author={Yanghao Wang, Long Chen},
    journal={arXiv preprint arXiv:2408.16266},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.16266},
    primaryClass={cs.CV}
}