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：我们提出了一种基于仿真推理范式的高效通用数据拼接算法。该算法以目标数据、源数据以及目标数据与源数据的分片划分为输入，通过学习源数据分片平均值的分布，使该分布的样本能够逼近目标数据的分片。我们采用一种可轻松并行化的模型，结合最新高效仿真推理技术，快速获取近似后验分布以满足实际应用需求。实验证明，该技术在音频与图像拼接任务中均表现有效。