BiProLoRA：面向真实场景复原的双层提示低秩适配
BiProLoRA: Bilevel Prompt LoRA for Real Scene Recovery
把预训练生成模型通过双层优化（超参数优化）联合调 LoRA 与提示词，并用自监督分布保真校准自编码通路，可直接迁移到去雨/去雾/低光照等真实退化复原任务中做合成到真实的适配。
原文依据 a bilevel joint modeling via hyperparameter optimization is further established, empowering robust synthetic-to-real adaptation
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论大型生成模型的出现大幅推进了合成域中基于学习的场景复原。然而，由于显著的分布差距，这些模型对真实场景的泛化能力较差，同时对复杂且未预见的退化适应不良。因此，亟需一种能够产生忠实复原结果并具备可靠泛化性的真实场景适应策略。为此，我们提出双层提示低秩适配（Bilevel Prompt LoRA），这是一种全新的学习范式，旨在有效地将预训练生成模型适配于真实场景复原。首先，我们引入一种自监督的分布保真学习方案，在与任务无关的真实分布下校准自编码通路，以提升纹理保真度。随后，进一步建立通过超参数优化的双层联合建模，利用低秩适配与提示词之间的互补优势促进相互增强，从而为可见与未见场景实现稳健的合成到真实适应。在多种真实恶劣场景上的大量评估证明了我们的优越性，全面的算法分析也验证了其有效性。
英文原文摘要 1195 字符
The emergence of large generative models has substantially advanced learning-based scene recovery in the synthetic domain. However, these models generalize poorly to real scenarios stemming from the significant distribution gap, alongside poor adaptation to complex and unforeseen degradations. Consequently, it is imperative to develop a real scene adaptation strategy that yields faithful restorations with reliable generalizability. To this end, we propose Bilevel Prompt LoRA, a novel learning paradigm designed to effectively adapt pre-trained generative models for real scene recovery. First, we introduce a self-supervised distribution-fidelity learning scheme to calibrate the autoencoding pathway under task-irrelevant real distributions to improve texture fidelity. Subsequently, a bilevel joint modeling via hyperparameter optimization is further established, empowering robust synthetic-to-real adaptation for both seen and unseen scenes by exploiting the complementary advantages between LoRA and Prompts to foster mutual promotion. Extensive evaluations on diverse real adverse scenarios demonstrate our superiority, with comprehensive algorithm analyses proving our effectiveness.