Rejection Sampling IMLE: Designing Priors for Better Few-Shot Image Synthesis

• URL: http://arxiv.org/abs/2409.17439v1
• Date: Thu, 26 Sep 2024 00:19:42 GMT
• Title: Rejection Sampling IMLE: Designing Priors for Better Few-Shot Image Synthesis
• Authors: Chirag Vashist, Shichong Peng, Ke Li
• Abstract summary: An emerging area of research aims to learn deep generative models with limited training data. We 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.
• Score: 7.234618871984921
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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