The main requisite for fine-grained recognition task is to focus on subtle
discriminative details that make the subordinate classes different from each
other. We note that existing methods implicitly address this requirement and
leave it to a data-driven pipeline to figure out what makes a subordinate class
different from the others. This results in two major limitations: First, the
network focuses on the most obvious distinctions between classes and overlooks
more subtle inter-class variations. Second, the chance of misclassifying a
given sample in any of the negative classes is considered equal, while in fact,
confusions generally occur among only the most similar classes. Here, we
propose to explicitly force the network to find the subtle differences among
closely related classes. In this pursuit, we introduce two key novelties that
can be easily plugged into existing end-to-end deep learning pipelines. On one
hand, we introduce diversification block which masks the most salient features
for an input to force the network to use more subtle cues for its correct
classification. Concurrently, we introduce a gradient-boosting loss function
that focuses only on the confusing classes for each sample and therefore moves
swiftly along the direction on the loss surface that seeks to resolve these
ambiguities. The synergy between these two blocks helps the network to learn
more effective feature representations. Comprehensive experiments are performed
on five challenging datasets. Our approach outperforms existing methods using
similar experimental setting on all five datasets.

该论文提出了引入多样性块和梯度提升损失函数两个关键创新点的细粒度识别方法，来解决先前方法在查找亚类别差异性和识别混淆类别上的局限性。在五个挑战性数据集上的全面实验表明，该方法在所有五个数据集上都比使用类似实验方案的现有方法表现更优。

细粒度识别：考虑相似类之间的细微差异

Fine-grained Recognition: Accounting for Subtle Differences between  Similar Classes

Analysis of over-parameterized neural networks has drawn significant
attention in recentyears. It was shown that such systems behave like convex
systems under various restrictedsettings, such as for two-level neural
networks, and when learning is only restricted locally inthe so-called neural
tangent kernel space around specialized initializations. However, there areno
theoretical techniques that can analyze fully trained deep neural networks
encountered inpractice. This paper solves this fundamental problem by
investigating such overparameterizeddeep neural networks when fully trained. We
generalize a new technique called neural feature repopulation, originally
introduced in (Fang et al., 2019a) for two-level neural networks, to analyze
deep neural networks. It is shown that under suitable representations,
overparameterized deep neural networks are inherently convex, and when
optimized, the system can learn effective features suitable for the underlying
learning task under mild conditions. This new analysis is consistent with
empirical observations that deep neural networks are capable of learning
efficient feature representations. Therefore, the highly unexpected result of
this paper can satisfactorily explain the practical success of deep neural
networks. Empirical studies confirm that predictions of our theory are
consistent with results observed in practice.

通过广义上名为神经元特征重新填充的技术，我们解决了分析训练后的深度神经网络的根本问题，并表明适当地表示时，过度参数化的深度神经网络固有地是凸性的，因此提供了解释深度神经网络实践成功的可靠理论依据.