Unsupervised domain adaptation (UDA) has attracted considerable attention, which transfers knowledge from a label-rich source domain to a related but unlabeled target domain. Reducing inter-domain differences has always been a crucial factor to improve performance in UDA, especially for tasks where there is a large gap between source and target domains. To this end, we propose a novel style-aware feature fusion method (SAFF) to bridge the large domain gap and transfer knowledge while alleviating the loss of class-discriminative information. Inspired by the human transitive inference and learning ability, a novel style-aware self-intermediate domain (SSID) is investigated to link two seemingly unrelated concepts through a series of intermediate auxiliary synthesized concepts. Specifically, we propose a novel learning strategy of SSID, which selects samples from both source and target domains as anchors, and then randomly fuses the object and style features of these anchors to generate labeled and style-rich intermediate auxiliary features for knowledge transfer. Moreover, we design an external memory bank to store and update specified labeled features to obtain stable class features and class-wise style features. Based on the proposed memory bank, the intra- and inter-domain loss functions are designed to improve the class recognition ability and feature compatibility, respectively. Meanwhile, we simulate the rich latent feature space of SSID by infinite sampling and the convergence of the loss function by mathematical theory. Finally, we conduct comprehensive experiments on commonly used domain adaptive benchmarks to evaluate the proposed SAFF, and the experimental results show that the proposed SAFF can be easily combined with different backbone networks and obtain better performance as a plug-in-plug-out module.
翻译:不受监督的域适应(UDA)吸引了相当多的注意力,将知识从标签丰富源域域转移至相关但没有标签的目标域。减少内部差异一直是提高UDA绩效的关键因素,特别是对于源域和目标域间存在巨大差距的任务而言。为此,我们提议采用一种创新的风格认知特性聚合方法(SAFF),以弥合巨大的域间差距和转移知识,同时减少类间差异信息的损失。在人类的中转感知和学习能力的启发下,一个具有新颖风格觉悟的自我中间介质域域域(SSID)受到调查,通过一系列中间辅助综合概念,将两个似乎无关的概念联系起来。具体地说,我们提出了一个新的SSID学习战略,从源域和目标域选取样本作为锚,然后随机结合这些锚的客体和风格特性,为知识转移产生标签化和样式丰富的中间辅助特性。此外,我们设计了一个外部记忆库,储存并更新了指定的标志性功能,以获得稳定的类间混合的域间内部统域域域域域域域内混合模型,我们使用的机能和机级级级级的机能特性分别显示我们所设计的机能损失和机能。