Boost-and-Skip: A Simple Guidance-Free Diffusion for Minority Generation

• URL: http://arxiv.org/abs/2502.06516v1
• Date: Mon, 10 Feb 2025 14:37:26 GMT
• Title: Boost-and-Skip: A Simple Guidance-Free Diffusion for Minority Generation
• Authors: Soobin Um, Beomsu Kim, Jong Chul Ye,
• Abstract summary: We present a powerful yet powerful guidance-free approach called Boost-and-Skip for generating minority samples using diffusion models. We highlight that these seemingly-trivial modifications are supported by solid theoretical and empirical evidence. Our experiments demonstrate that Boost-and-Skip greatly enhances the capability of generating minority samples, even rivaling guidance-based state-of-the-art approaches.
• Score: 57.19995625893062
• License:
• Abstract: Minority samples are underrepresented instances located in low-density regions of a data manifold, and are valuable in many generative AI applications, such as data augmentation, creative content generation, etc. Unfortunately, existing diffusion-based minority generators often rely on computationally expensive guidance dedicated for minority generation. To address this, here we present a simple yet powerful guidance-free approach called Boost-and-Skip for generating minority samples using diffusion models. The key advantage of our framework requires only two minimal changes to standard generative processes: (i) variance-boosted initialization and (ii) timestep skipping. We highlight that these seemingly-trivial modifications are supported by solid theoretical and empirical evidence, thereby effectively promoting emergence of underrepresented minority features. Our comprehensive experiments demonstrate that Boost-and-Skip greatly enhances the capability of generating minority samples, even rivaling guidance-based state-of-the-art approaches while requiring significantly fewer computations.

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