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DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.1109/cvpr.2018.58ACPO) was visible in the surrounding text but could not be confirmed against doi.org as printed.
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The Unreasonable Effectiveness of Deep Features as a Perceptual Metric. InIEEE/CVF Conference on Computer Vision and Pattern Recognition. 586–595. doi:10.1109/CVPR.2018.58 ACPO: Anchor-Constrained Perceptual Optimization Yang et al. Supplementary Material This supplementary material complements the main paper by pro- viding an expanded set of visual evaluations and supplementary experimental analyses. Section 1 presents extended qualitative com- parisons for our baseline experiments, offering further insights into the perceptual strengths of our approach. Section 2 showcases a broader range of visual examples illustrating the method’s gen- eralization capabilities across different datasets and diverse text prompts. Finally, Section 3 presents extended experimental anal- yses, including more comprehensive ablation studies conducted on the Stable Diffusion (SD) architecture to evaluate the specific impacts of timestep intervention, quality weighting, and anchor loss. A Extended Qualitative Results This section provides additional qualitative evidence to support the effectiveness of the Anchor-Constrained Perceptual Optimization (ACPO) framework. Following the logic established in the main paper, we showcase results across two dimensions: general percep- tual quality enhancement on unconditional models and semantic alignment refinement on text-to-image models. A.1 Additional Visual Evidence for Perceptual Quality Figure 5 presents a comparative visualization between the base- li
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