  Ensuring logical consistency in predictions is a crucial yet overlooked
aspect in multi-attribute classification. We explore the potential reasons for
this oversight and introduce two pressing challenges to the field: 1) How can
we ensure that a model, when trained with data checked for logical consistency,
yields predictions that are logically consistent? 2) How can we achieve the
same with data that hasn't undergone logical consistency checks? Minimizing
manual effort is also essential for enhancing automation. To address these
challenges, we introduce two datasets, FH41K and CelebA-logic, and propose
LogicNet, an adversarial training framework that learns the logical
relationships between attributes. Accuracy of LogicNet surpasses that of the
next-best approach by 23.05%, 9.96%, and 1.71% on FH37K, FH41K, and
CelebA-logic, respectively. In real-world case analysis, our approach can
achieve a reduction of more than 50% in the average number of failed cases
compared to other methods.
