We introduce an efficient algorithm for general data mosaicing, based on the simulation-based inference paradigm. Our algorithm takes as input a target datum, source data, and partitions of the target and source data into fragments, learning distributions over averages of fragments of the source data such that samples from those distributions approximate fragments of the target datum. We utilize a model that can be trivially parallelized in conjunction with the latest advances in efficient simulation-based inference in order to find approximate posteriors fast enough for use in practical applications. We demonstrate our technique is effective in both audio and image mosaicing problems.
翻译:我们采用了一种基于模拟推理模式的通用数据拼凑有效算法。我们的算法将目标数据、源数据以及目标数据和源数据分割成碎片,学习源数据碎片平均分布,以了解源数据碎片平均分布,使分布结果的样本大致相当于目标基准的碎片。我们使用的模型可以与高效模拟推理的最新进展相平行,以便快速找到近似近似近似近象物,用于实际应用。我们证明我们的技术在音频和图像组合问题上都是有效的。