A major challenge in Fine-Grained Visual Classification (FGVC) is distinguishing various categories with high inter-class similarity by learning the feature that differentiate the details. Conventional cross entropy trained Convolutional Neural Network (CNN) fails this challenge as it may suffer from producing inter-class invariant features in FGVC. In this work, we innovatively propose to regularize the training of CNN by enforcing the uniqueness of the features to each category from an information theoretic perspective. To achieve this goal, we formulate a minimax loss based on a game theoretic framework, where a Nash equilibria is proved to be consistent with this regularization objective. Besides, to prevent from a feasible solution of minimax loss that may produce redundant features, we present a Feature Redundancy Loss (FRL) based on normalized inner product between each selected feature map pair to complement the proposed minimax loss. Superior experimental results on several influential benchmarks along with visualization show that our method gives full play to the performance of the baseline model without additional computation and achieves comparable results with state-of-the-art models.

翻译：精细视觉分类(FGVC)中的一项重大挑战是,通过学习区分细节的特征,区分类别间相似程度较高的不同类别。常规交叉星系经培训的革命神经网络(CNN)未能应对这一挑战,因为它可能因在FGVC中产生阶级间差异性特征而受到影响。在这项工作中,我们创新地提议从信息理论的角度对每个类别实施特征的独特性,从而规范对CNN的培训。为了实现这一目标,我们根据游戏理论框架制定了一个微缩损失,在这个框架中,Nash equilibraria证明符合这一规范目标。此外,为了防止对可能产生冗余特征的微型损失采取可行的解决办法,我们根据每个选定的地貌图配对的正常内产,提出了地貌重复损失,以补充拟议的微缩缩损失。若干有影响力的基准和可视化的高级实验结果表明,我们的方法在不进行额外计算的情况下充分运用了基线模型的性能,并取得了与最新模型相近的结果。