Few-Shot Image Generation by Conditional Relaxing Diffusion Inversion
<|reference_start|>Few-Shot Image Generation by Conditional Relaxing Diffusion Inversion: In the field of Few-Shot Image Generation (FSIG) using Deep Generative Models (DGMs), accurately estimating the distribution of target domain with minimal samples poses a significant challenge. This requires a method that can both capture the broad diversity and the true characteristics of the target domain distribution. We present Conditional Relaxing Diffusion Inversion (CRDI), an innovative `training-free' approach designed to enhance distribution diversity in synthetic image generation. Distinct from conventional methods, CRDI does not rely on fine-tuning based on only a few samples. Instead, it focuses on reconstructing each target image instance and expanding diversity through few-shot learning. The approach initiates by identifying a Sample-wise Guidance Embedding (SGE) for the diffusion model, which serves a purpose analogous to the explicit latent codes in certain Generative Adversarial Network (GAN) models. Subsequently, the method involves a scheduler that progressively introduces perturbations to the SGE, thereby augmenting diversity. Comprehensive experiments demonstrates that our method surpasses GAN-based reconstruction techniques and equals state-of-the-art (SOTA) FSIG methods in performance. Additionally, it effectively mitigates overfitting and catastrophic forgetting, common drawbacks of fine-tuning approaches.<|reference_end|>
@article{cao2024few-shot,
    title={Few-Shot Image Generation by Conditional Relaxing Diffusion Inversion},
    author={Yu Cao, Shaogang Gong},
    journal={arXiv preprint arXiv:2407.07249},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.07249},
    primaryClass={cs.CV}
}