Collecting paired training data is difficult in practice, but the unpaired samples broadly exist. Current approaches aim at generating synthesized training data from unpaired samples by exploring the relationship between the corrupted and clean data. This work proposes LUD-VAE, a deep generative method to learn the joint probability density function from data sampled from marginal distributions. Our approach is based on a carefully designed probabilistic graphical model in which the clean and corrupted data domains are conditionally independent. Using variational inference, we maximize the evidence lower bound (ELBO) to estimate the joint probability density function. Furthermore, we show that the ELBO is computable without paired samples under the inference invariant assumption. This property provides the mathematical rationale of our approach in the unpaired setting. Finally, we apply our method to real-world image denoising, super-resolution, and low-light image enhancement tasks and train the models using the synthetic data generated by the LUD-VAE. Experimental results validate the advantages of our method over other approaches.
翻译:收集对齐培训数据在实践中是困难的,但未腐蚀的样本广泛存在。 目前的方法旨在通过探索腐败和干净数据之间的关系,从未腐蚀和干净的样本中生成综合培训数据。 这项工作提出LUD- VAE, 这是一种从边际分布抽样数据中学习共同概率密度函数的深层基因化方法。 我们的方法基于一种精心设计的概率化图形模型,其中清洁和腐败的数据领域有条件地独立。 我们使用不同的推论, 尽量扩大证据下限( ELBO) 来估计联合概率密度函数。 此外, 我们还表明, ELBO 可以在不进行配对的样本的情况下进行可比较。 这个属性提供了我们在未腐蚀的环境下采用的方法的数学原理。 最后, 我们用我们的方法来应用真实世界图像的淡化、 超分辨率 和 低光度图像增强任务, 并利用LUD- VAE 生成的合成数据来培训模型。 实验结果证实了我们的方法优于其他方法的优势 。