直接上升合成：揭示判别模型中隐藏的生成能力
Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in Discriminative Models
AI总结：
该研究挑战判别与生成模型的根本区别，提出直接上升合成（DAS）方法，通过多分辨率优化CLIP表示，在无额外训练下实现高质量图像合成，支持多样应用并揭示判别模型隐藏的生成能力。
摘要：
我们证明判别模型本身蕴含强大的生成能力，挑战了判别与生成架构间的根本区别。我们的方法直接上升合成（DAS）通过对CLIP模型表示的多分辨率优化揭示这些潜在能力。传统反转尝试产生对抗模式，而DAS通过在多空间尺度（1x1至224x224）分解优化实现高质量图像合成，无需额外训练。该方法不仅支持从文本到图像生成到风格迁移的多样应用，还保持自然图像统计（1/f²谱），并引导生成远离非鲁棒的对抗模式。我们的结果表明，标准判别模型编码的生成知识比以往认知的丰富得多，为模型可解释性及对抗样本与自然图像合成的关系提供新视角。
英文摘要：
We demonstrate that discriminative models inherently contain powerful generative capabilities, challenging the fundamental distinction between discriminative and generative architectures. Our method, Direct Ascent Synthesis (DAS), reveals these latent capabilities through multi-resolution optimization of CLIP model representations. While traditional inversion attempts produce adversarial patterns, DAS achieves high-quality image synthesis by decomposing optimization across multiple spatial scales (1x1 to 224x224), requiring no additional training. This approach not only enables diverse applications -- from text-to-image generation to style transfer -- but maintains natural image statistics ($1/f^2$ spectrum) and guides the generation away from non-robust adversarial patterns. Our results demonstrate that standard discriminative models encode substantially richer generative knowledge than previously recognized, providing new perspectives on model interpretability and the relationship between adversarial examples and natural image synthesis.
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