自最优传输特征变换
The Self-Optimal-Transport Feature Transform
摘要：
自最优传输（SOT）特征转换旨在升级数据实例的特征集，以促进下游匹配或分组相关任务。变换后的集合编码了实例特征之间的高阶关系的丰富表示。变换后的特征之间的距离捕获它们的直接原始相似性以及它们与集合中的其他特征的相似性的第三方协议。一个特定的最小成本最大流分数匹配问题，其熵正则化的版本可以近似的最佳运输（OT）优化，导致我们的转导变换是有效的，可微的，等变的，无参数和概率解释。从经验上讲，转换在使用中非常有效和灵活，在各种任务和培训计划中不断改进它所插入的网络。我们证明了它的优点，通过无监督聚类和它的效率和广泛的适用性的问题，Few-Shot分类，与国家的最先进的结果，和大规模的人重新识别。
英文摘要：
The Self-Optimal-Transport (SOT) feature transform is designed to upgrade the set of features of a data instance to facilitate downstream matching or grouping related tasks. The transformed set encodes a rich representation of high order relations between the instance features. Distances between transformed features capture their direct original similarity and their third party agreement regarding similarity to other features in the set. A particular min-cost-max-flow fractional matching problem, whose entropy regularized version can be approximated by an optimal transport (OT) optimization, results in our transductive transform which is efficient, differentiable, equivariant, parameterless and probabilistically interpretable. Empirically, the transform is highly effective and flexible in its use, consistently improving networks it is inserted into, in a variety of tasks and training schemes. We demonstrate its merits through the problem of unsupervised clustering and its efficiency and wide applicability for few-shot-classification, with state-of-the-art results, and large-scale person re-identification.
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。
