Summary of Rejection Sampling Imle: Designing Priors For Better Few-shot Image Synthesis, by Chirag Vashist et al.
Rejection Sampling IMLE: Designing Priors for Better Few-Shot Image Synthesis
by Chirag Vashist, Shichong Peng, Ke Li
First submitted to arxiv on: 26 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Recent advancements in deep generative models have been hindered by the need for large amounts of training data. Generative Adversarial Networks (GANs) and diffusion models, while successful, struggle when trained on limited data. Implicit Maximum Likelihood Estimation (IMLE), a promising technique, has achieved state-of-the-art performance in few-shot settings. However, current IMLE-based approaches face challenges due to the mismatch between latent codes used for training and those drawn during inference. Our research presents a theoretical solution to this issue and proposes RS-IMLE, a novel approach that modifies the prior distribution used for training. This leads to significantly improved image generation quality compared to existing GAN and IMLE-based methods, as validated by experiments on nine few-shot image datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Researchers are trying to create artificial intelligence models that can generate new images from very little data. Current methods like Generative Adversarial Networks (GANs) and diffusion models need lots of information to work well, but they get worse when given only a small amount. A new technique called IMLE has helped improve performance in these situations. However, there’s still a problem: the way we choose which images to use for training doesn’t match how we draw new images later on. Our study finds a solution to this issue and proposes a new approach called RS-IMLE. This method generates higher-quality images compared to existing methods, as tested on nine different datasets. |
Keywords
» Artificial intelligence » Diffusion » Few shot » Gan » Image generation » Inference » Likelihood
