Recent advances in fine-grained representation learning leverage local-to-global (emergent) relationships for achieving state-of-the-art results. The relational representations relied upon by such methods, however, are abstract. We aim to deconstruct this abstraction by expressing them as interpretable graphs over image views. We begin by theoretically showing that abstract relational representations are nothing but a way of recovering transitive relationships among local views. Based on this, we design Transitivity Recovering Decompositions (TRD), a graph-space search algorithm that identifies interpretable equivalents of abstract emergent relationships at both instance and class levels, and with no post-hoc computations. We additionally show that TRD is provably robust to noisy views, with empirical evidence also supporting this finding. The latter allows TRD to perform at par or even better than the state-of-the-art, while being fully interpretable. Implementation is available at https://github.com/abhrac/trd.

最近，在精细化表示学习方面取得的进展利用了局部到全局（新兴）关系，以取得最先进的结果。然而，这种方法依赖的关系表示是抽象的。我们旨在通过将其表达为图形视图上的可解释图来解构这种抽象。基于此，我们设计了Transitivity Recovering Decompositions（TRD），一种图空间搜索算法，它在实例和类别级别上识别了抽象新兴关系的可解释等价物，并且不产生后处理计算。我们还展示了TRD对于嘈杂视图的鲁棒性是可证明的，并且还有经验证据支持这一发现。后者使TRD能够与最先进技术相媲美甚至更好，同时具有完全可解释性。该实施代码可在链接中找到。

可解释和稳健的细粒度关系的推导:传递性恢复分解