Computer Science > Computer Vision and Pattern Recognition
[Submitted on 23 Jun 2022 (v1), last revised 20 Sep 2022 (this version, v5)]
Title:Entropy-driven Sampling and Training Scheme for Conditional Diffusion Generation
View PDFAbstract:Denoising Diffusion Probabilistic Model (DDPM) is able to make flexible conditional image generation from prior noise to real data, by introducing an independent noise-aware classifier to provide conditional gradient guidance at each time step of denoising process. However, due to the ability of classifier to easily discriminate an incompletely generated image only with high-level structure, the gradient, which is a kind of class information guidance, tends to vanish early, leading to the collapse from conditional generation process into the unconditional process. To address this problem, we propose two simple but effective approaches from two perspectives. For sampling procedure, we introduce the entropy of predicted distribution as the measure of guidance vanishing level and propose an entropy-aware scaling method to adaptively recover the conditional semantic guidance. For training stage, we propose the entropy-aware optimization objectives to alleviate the overconfident prediction for noisy this http URL ImageNet1000 256x256, with our proposed sampling scheme and trained classifier, the pretrained conditional and unconditional DDPM model can achieve 10.89% (4.59 to 4.09) and 43.5% (12 to 6.78) FID improvement respectively. The code is available at this https URL.
Submission history
From: Shengming Li [view email][v1] Thu, 23 Jun 2022 04:10:23 UTC (38,258 KB)
[v2] Fri, 24 Jun 2022 05:02:21 UTC (38,320 KB)
[v3] Mon, 27 Jun 2022 03:29:51 UTC (38,317 KB)
[v4] Tue, 23 Aug 2022 03:48:03 UTC (38,303 KB)
[v5] Tue, 20 Sep 2022 02:34:12 UTC (38,301 KB)
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