论文标题
具有基于模拟的推理的有效数据镶嵌
Efficient Data Mosaicing with Simulation-based Inference
论文作者
论文摘要
我们基于基于仿真的推理范例，引入了一种用于一般数据镶嵌的有效算法。我们的算法将目标数据，源数据和源数据和源数据的分区输入到片段中，对源数据的平均值进行分布，从而使这些分布的样本近似于目标数据的片段。我们利用一个模型，该模型可以与有效仿真推理的最新进展结合使用，以便找到足够快的后代，以便在实际应用中使用。我们证明我们的技术在音频和图像镶嵌问题方面都是有效的。
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.
