Representational learning hinges on the task of unraveling the set of underlying explanatory factors of variation in data. In this work, we operate in the setting where limited information is known about the data in the form of groupings, or set membership, where the underlying factors of variation is restricted to a subset. Our goal is to learn representations which isolate the factors of variation that are common across the groupings. Our key insight is the use of cycle consistency across sets(CCS) between the learned embeddings of images belonging to different sets. In contrast to other methods utilizing set supervision, CCS can be applied with significantly fewer constraints on the factors of variation, across a remarkably broad range of settings, and only utilizing set membership for some fraction of the training data. By curating datasets from Shapes3D, we quantify the effectiveness of CCS through mutual information between the learned representations and the known generative factors. In addition, we demonstrate the applicability of CCS to the tasks of digit style isolation and synthetic-to-real object pose transfer and compare to generative approaches utilizing the same supervision.

中文翻译：

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反复进行：各组之间的循环一致性，以隔离变化因素

代表性学习取决于解开数据变化的一组潜在解释因素的任务。在这项工作中，我们在这样的环境中工作：以分组或集合成员身份的形式了解有关数据的有限信息，其中将潜在的变化因素限制为一个子集。我们的目标是学习表示法，以隔离各组中常见的变异因素。我们的主要见解是在学习的属于不同集合的图像嵌入之间，跨集合（CCS）使用循环一致性。与其他利用集合监督的方法相比，CCS可以在非常广泛的设置范围内，对变异因素的约束大大减少，并且仅对训练数据的一部分使用集合成员资格。通过整理来自Shapes3D的数据集，我们通过学习的表示与已知的生成因子之间的相互信息来量化CCS的有效性。此外，我们演示了CCS在数字样式隔离和从合成到真实的对象姿态转换任务中的适用性，并与使用相同监督的生成方法进行了比较。