SPI-GAN: Denoising Diffusion GANs with Straight-Path Interpolations

• URL: http://arxiv.org/abs/2206.14464v3
• Date: Thu, 14 Mar 2024 05:28:04 GMT
• Title: SPI-GAN: Denoising Diffusion GANs with Straight-Path Interpolations
• Authors: Jinsung Jeon, Noseong Park,
• Abstract summary: We present an enhanced GAN-based denoising method, called SPI-GAN, using our proposed straight-path definition. SPI-GAN is one of the best-balanced models among the sampling quality, diversity, and time for CIFAR-10, and CelebA-HQ-256.
• Score: 27.487728842037935
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Score-based generative models (SGMs) show the state-of-the-art sampling quality and diversity. However, their training/sampling complexity is notoriously high due to the highly complicated forward/reverse processes, so they are not suitable for resource-limited settings. To solving this problem, learning a simpler process is gathering much attention currently. We present an enhanced GAN-based denoising method, called SPI-GAN, using our proposed straight-path interpolation definition. To this end, we propose a GAN architecture i) denoising through the straight-path and ii) characterized by a continuous mapping neural network for imitating the denoising path. This approach drastically reduces the sampling time while achieving as high sampling quality and diversity as SGMs. As a result, SPI-GAN is one of the best-balanced models among the sampling quality, diversity, and time for CIFAR-10, and CelebA-HQ-256.

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