学习ABC：在弱监督下隔离变异因素的近似双射对应
Learning ABCs: Approximate Bijective Correspondence for isolating factors of variation with weak supervision
摘要：
表征学习是大多数深度学习应用程序的支柱，而学习表征的价值与其关于不同变异因素的信息内容密切相关。找到好的表示取决于监督的性质和学习算法。我们提出了一种新的算法，利用一种弱形式的监督，其中的数据被划分成集根据某些非活动（共同）的变化因素，这是不变的每个集合的元素。我们的关键见解是，通过寻求不同集合的元素之间的对应关系，我们学习了强表示，排除了非活动的变化因素，并隔离了所有集合中变化的活动因素。由于专注于活跃因素，我们的方法可以利用集合监督和完全无监督数据的混合，这些数据甚至可以属于不同的领域。我们解决了具有挑战性的问题，合成到真实对象的姿态转移，没有任何构成的注释，通过隔离的姿态信息，概括到类别级别，并跨越合成/真实域的差距。该方法还可以通过加强中间表示来提高监督设置中的性能，以及在实际可实现的场景中使用集监督自然图像进行操作，其中数量有限并且变化的干扰因素更丰富。
英文摘要：
Representational learning forms the backbone of most deep learning applications, and the value of a learned representation is intimately tied to its information content regarding different factors of variation. Finding good representations depends on the nature of supervision and the learning algorithm. We propose a novel algorithm that utilizes a weak form of supervision where the data is partitioned into sets according to certain inactive (common) factors of variation which are invariant across elements of each set. Our key insight is that by seeking correspondence between elements of different sets, we learn strong representations that exclude the inactive factors of variation and isolate the active factors that vary within all sets. As a consequence of focusing on the active factors, our method can leverage a mix of set-supervised and wholly unsupervised data, which can even belong to a different domain. We tackle the challenging problem of synthetic-to-real object pose transfer, without pose annotations on anything, by isolating pose information which generalizes to the category level and across the synthetic/real domain gap. The method can also boost performance in supervised settings, by strengthening intermediate representations, as well as operate in practically attainable scenarios with set-supervised natural images, where quantity is limited and nuisance factors of variation are more plentiful.
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。
