分叉或失败：使用多对一映射的周期一致性训练
Fork or Fail: Cycle-Consistent Training with Many-to-One Mappings
摘要：
循环一致性训练被广泛用于联合学习两个感兴趣的域之间的正向和反向映射，而无需在每个域内收集匹配对的繁琐要求。在这方面，隐含的假设是存在（至少近似地）地面实况双射，使得来自任一域的给定输入可以从相应映射的连续应用准确地重建。但在许多应用中，不可能存在这样的双射，并且大的重建误差可能会损害周期一致性训练的成功。作为这种限制的一个重要实例，我们考虑实际相关的情况下，存在一个多对一或满射域之间的映射。为了解决这个问题，我们开发了一个有条件的变分自编码器（CVAE）的方法，可以被视为转换满射映射到隐式双射，从而在两个方向上的重建误差可以最小化，并作为一个自然的副产品，现实的输出多样性可以在一对多的方向。作为理论动机，我们分析了一个简化的情况下，建议CVAE为基础的能量函数的最小值与地面真理满射映射的恢复对齐。在经验方面，我们认为一个合成的图像数据集与已知的地面真理，以及一个真实世界的应用程序，涉及自然语言生成从知识图谱，反之亦然，一个原型满射的情况。对于后者，我们的CVAE管道可以在循环训练期间捕获这种多对一映射，同时促进图形到文本任务的纹理多样性。我们的代码可在github.com/QipengGuo/CycleGT上获得 * 本文的精简版已被AISTATS 2021接受。此版本包含其他内容和更新。
英文摘要：
Cycle-consistent training is widely used for jointly learning a forward and inverse mapping between two domains of interest without the cumbersome requirement of collecting matched pairs within each domain. In this regard, the implicit assumption is that there exists (at least approximately) a ground-truth bijection such that a given input from either domain can be accurately reconstructed from successive application of the respective mappings. But in many applications no such bijection can be expected to exist and large reconstruction errors can compromise the success of cycle-consistent training. As one important instance of this limitation, we consider practically-relevant situations where there exists a many-to-one or surjective mapping between domains. To address this regime, we develop a conditional variational autoencoder (CVAE) approach that can be viewed as converting surjective mappings to implicit bijections whereby reconstruction errors in both directions can be minimized, and as a natural byproduct, realistic output diversity can be obtained in the one-to-many direction. As theoretical motivation, we analyze a simplified scenario whereby minima of the proposed CVAE-based energy function align with the recovery of ground-truth surjective mappings. On the empirical side, we consider a synthetic image dataset with known ground-truth, as well as a real-world application involving natural language generation from knowledge graphs and vice versa, a prototypical surjective case. For the latter, our CVAE pipeline can capture such many-to-one mappings during cycle training while promoting textural diversity for graph-to-text tasks. Our code is available at github.com/QipengGuo/CycleGT *A condensed version of this paper has been accepted to AISTATS 2021. This version contains additional content and updates.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
