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

LogicNet: A Logical Consistency Embedded Face Attribute Learning Network