How to improve discriminative feature learning is central in classification. Existing works address this problem by explicitly increasing inter-class separability and intra-class compactness by constructing positive and negative pairs for contrastive learning or posing tighter class separating margins. These methods do not exploit the similarity between different classes as they adhere to independent identical distributions assumption in data. In this paper, we embrace the real-world data distribution setting in that some classes share semantic overlaps due to their similar appearances or concepts. Regarding this hypothesis, we propose a novel regularization to improve discriminative learning. We first calibrate the estimated highest likelihood of one sample based on its semantically neighboring classes, then encourage the overall likelihood predictions to be deterministic by imposing an adaptive exponential penalty. As the gradient of the proposed method is roughly proportional to the uncertainty of the predicted likelihoods, we name it adaptive discriminative regularization (ADR), trained along with a standard cross entropy loss in classification. Extensive experiments demonstrate that it can yield consistent and non-trivial performance improvements in a variety of visual classification tasks (over 10 benchmarks). Furthermore, we find it is robust to long-tailed and noisy label data distribution. Its flexible design enables its compatibility with mainstream classification architectures and losses.

如何提高判别性特征学习是分类的核心。现有的工作通过构建用于对比学习的正负对或提出更严格的类分离裕度来显式地增加类间可分离性和类内紧凑性来解决这个问题。这些方法没有利用不同类之间的相似性，因为它们遵循数据中的独立相同分布假设。在本文中，我们接受现实世界的数据分布设置，因为某些类由于其相似的外观或概念而共享语义重叠。关于这个假设，我们提出了一种新颖的正则化来改善判别性学习。我们首先根据一个样本的语义相邻类来校准其估计的最高似然度，然后通过施加自适应指数惩罚来鼓励总体似然度预测具有确定性。由于所提出方法的梯度大致与预测可能性的不确定性成正比，因此我们将其命名为自适应判别正则化（ADR），与分类中的标准交叉熵损失一起训练。大量实验表明，它可以在各种视觉分类任务（超过 10 个基准测试）中产生一致且重要的性能改进。此外，我们发现它对于长尾和噪声标签数据分布具有鲁棒性。其灵活的设计使其能够与主流分类架构和损耗兼容。