论文标题
得分指导的中间层优化：反向问题的快速倾斜混合
Score-Guided Intermediate Layer Optimization: Fast Langevin Mixing for Inverse Problems
论文作者
论文摘要
我们证明了快速混合并表征了langevin算法的固定分布，以颠倒随机加权DNN发电机。该结果将手和Voroninski的工作从有效的反转到有效的后部采样。实际上，为了提高表达性，我们建议在预训练的生成模型的潜在空间中进行后验采样。为了实现这一目标，我们在StyleGAN-2的潜在空间中训练基于分数的模型，并使用它来解决反问题。我们的框架，得分引导的中间层优化（SGILO），通过用中间层中的生成性替换稀疏正则化来扩展先前的工作。在实验上，我们比以前的最新面前获得了显着改善，尤其是在低测量方案中。
We prove fast mixing and characterize the stationary distribution of the Langevin Algorithm for inverting random weighted DNN generators. This result extends the work of Hand and Voroninski from efficient inversion to efficient posterior sampling. In practice, to allow for increased expressivity, we propose to do posterior sampling in the latent space of a pre-trained generative model. To achieve that, we train a score-based model in the latent space of a StyleGAN-2 and we use it to solve inverse problems. Our framework, Score-Guided Intermediate Layer Optimization (SGILO), extends prior work by replacing the sparsity regularization with a generative prior in the intermediate layer. Experimentally, we obtain significant improvements over the previous state-of-the-art, especially in the low measurement regime.
