Rejection Sampling IMLE: Designing Priors for Better Few-Shot Image Synthesis
<|reference_start|>Rejection Sampling IMLE: Designing Priors for Better Few-Shot Image Synthesis: An emerging area of research aims to learn deep generative models with limited training data. Prior generative models like GANs and diffusion models require a lot of data to perform well, and their performance degrades when they are trained on only a small amount of data. A recent technique called Implicit Maximum Likelihood Estimation (IMLE) has been adapted to the few-shot setting, achieving state-of-the-art performance. However, current IMLE-based approaches encounter challenges due to inadequate correspondence between the latent codes selected for training and those drawn during inference. This results in suboptimal test-time performance. We theoretically show a way to address this issue and propose RS-IMLE, a novel approach that changes the prior distribution used for training. This leads to substantially higher quality image generation compared to existing GAN and IMLE-based methods, as validated by comprehensive experiments conducted on nine few-shot image datasets.<|reference_end|>
@article{vashist2024rejection,
    title={Rejection Sampling IMLE: Designing Priors for Better Few-Shot Image
  Synthesis},
    author={Chirag Vashist, Shichong Peng, Ke Li},
    journal={arXiv preprint arXiv:2409.17439},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.17439},
    primaryClass={cs.CV cs.LG}
}