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In distinction, inference-time solutions purpose to detect or suppress memorization without modifying design parameters chen2024towards; jain2025classifier; hintersdorf2024finding; chen2025exploring; ren2024unveiling, giving an productive substitute.
Initialization through Frequency Decomposition. Drawing inspiration from modern works demonstrating that initialization influences memorization actions hanadjusting; wu2024freeinit, we adopt a frequency decomposed initialization strategy that blends the very low-frequency factors of a reference image with substantial-frequency Gaussian noise.
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By injecting semantically aligned features from non-memorized reference pictures, CAPTAIN suppresses memorized material without sacrificing visual high quality or alignment with the conditioning prompt. Comprehensive experiments exhibit that CAPTAIN achieves stronger memorization mitigation than current inference-time baselines even though keeping competitive semantic fidelity.
(iii) Semantic attribute injection for memorization mitigation. We suggest a localized attribute injection system that replaces memorized latent areas with semantically aligned characteristics extracted from non-memorized reference visuals, steering generation clear of memorized outputs though maintaining prompt fidelity and Visible high-quality.
Present mitigation procedures might be broadly classified into schooling-time and inference-time strategies. Schooling-time solutions ordinarily have to have use of the original dataset and enormous computational assets, limiting their practicality wen2024detecting.
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