Pattern recognition is significantly challenging in real-world scenarios by the variability of visual statistics. Therefore, most existing algorithms relying on the independent identically distributed assumption of training and test data suffer from the poor generalization capability of inference on unseen testing datasets. Although numerous studies, including domain discriminator or domain-invariant feature learning, are proposed to alleviate this problem, the data-driven property and lack of interpretation of their principle throw researchers and developers off. Consequently, this dilemma incurs us to rethink the essence of networks’ generalization. An observation that visual patterns cannot be discriminative after style transfer inspires us to take careful consideration of the importance of style features and content features. Does the style information related to the domain bias? How to effectively disentangle content and style features across domains? In this article, we first investigate the effect of feature normalization on domain adaptation. Based on it, we propose a novel normalization module to adaptively leverage the propagated information through each channel and batch of features called disentangling batch instance normalization (D-BIN). In this module, we explicitly explore domain-specific and domaininvariant feature disentanglement. We maneuver contrastive learning to encourage images with the same semantics from different domains to have similar content representations while having dissimilar style representations. Furthermore, we construct both self-form and dual-form regularizers for preserving the mutual information (MI) between feature representations of the normalization layer in order to compensate for the loss of discriminative information and effectively match the distributions across domains. D-BIN and the constrained term can be simply plugged into state-of-the-art (SOTA) networks to improve their performance. In the end, experiments, including domain adaptation and generalization, conducted on different datasets have proven their effectiveness.

中文翻译：

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D-BIN：用于域适应的通用解缠批量实例标准化


由于视觉统计数据的可变性，模式识别在现实场景中面临着巨大的挑战。因此，大多数现有的算法依赖于训练和测试数据的独立同分布假设，但对未见过的测试数据集的推理泛化能力较差。尽管提出了许多研究（包括域鉴别器或域不变特征学习）来缓解这个问题，但数据驱动的特性和缺乏对其原理的解释让研究人员和开发人员望而却步。因此，这种困境促使我们重新思考网络泛化的本质。风格迁移后视觉模式不能具有区分性的观察启发我们仔细考虑风格特征和内容特征的重要性。风格信息与领域偏差相关吗？如何有效地理清跨领域的内容和风格特征？在本文中，我们首先研究特征归一化对领域适应的影响。基于此，我们提出了一种新颖的标准化模块，可以自适应地利用通过每个通道和批次特征传播的信息，称为解缠批量实例标准化（D-BIN）。在本模块中，我们明确探索特定领域和领域不变的特征解缠结。我们通过对比学习来鼓励来自不同领域的具有相同语义的图像具有相似的内容表示，同时具有不同的风格表示。 此外，我们构建了自形式和对偶形式正则化器来保留归一化层的特征表示之间的互信息（MI），以补偿判别信息的损失并有效地匹配跨域的分布。 D-BIN 和约束项可以简单地插入最先进的 (SOTA) 网络中以提高其性能。最后，在不同数据集上进行的包括领域适应和泛化在内的实验证明了其有效性。