Deep learning models usually require a large amount of labeled data to achieve satisfactory performance. In multimedia analysis, domain adaptation studies the problem of cross-domain knowledge transfer from a label rich source domain to a label scarce target domain, thus potentially alleviates the annotation requirement for deep learning models. However, we find that contemporary domain adaptation methods for cross-domain image understanding perform poorly when source domain is noisy. Weakly Supervised Domain Adaptation (WSDA) studies the domain adaptation problem under the scenario where source data can be noisy. Prior methods on WSDA remove noisy source data and align the marginal distribution across domains without considering the fine-grained semantic structure in the embedding space, which have the problem of class misalignment, e.g., features of cats in the target domain might be mapped near features of dogs in the source domain. In this paper, we propose a novel method, termed Noise Tolerant Domain Adaptation, for WSDA. Specifically, we adopt the cluster assumption and learn cluster discriminatively with class prototypes in the embedding space. We propose to leverage the location information of the data points in the embedding space and model the location information with a Gaussian mixture model to identify noisy source data. We then design a network which incorporates the Gaussian mixture noise model as a sub-module for unsupervised noise removal and propose a novel cluster-level adversarial adaptation method which aligns unlabeled target data with the less noisy class prototypes for mapping the semantic structure across domains. We conduct extensive experiments to evaluate the effectiveness of our method on both general images and medical images from COVID-19 and e-commerce datasets. The results show that our method significantly outperforms state-of-the-art WSDA methods.

翻译：深层学习模型通常需要大量的标签数据才能达到令人满意的性能。 在多媒体分析中, 域适应研究跨域知识从标签丰富源域向标签稀有的目标域转移的问题, 从而有可能减轻深层学习模型的批注要求。 然而, 我们发现当源域噪音时, 交叉域图像理解的当代域适应方法效果不佳。 微弱的监管多曼适应( WSDA) 研究源数据可能吵闹的情景下的域适应问题。 WSDA 先前的方法删除了噪音源数据, 并调整了跨域的边际分布, 而没有考虑嵌入空间的精细度结构的语义结构, 因而有可能减轻深度学习。 目标域的猫特性可能会在源域内的狗特征附近绘制。 我们为WSDADA提议一种新型方法, 称为Noise Tolerant Domicald 适应。 我们采用了集群假设的群集假设, 和在嵌入空间的类级结构模型中, 我们提议在嵌入域域域域域内的非级数据定位数据定位点上, 将我们的数据模型显示一个模型的模化的模版值数据模型,, 将数据模型显示一个模型的模版值的模值的模值的模值的模版值的模版值的模版值数据模型, 值的模值的模值的模值的模值的模值的模制的模制的模制数据值的模值显示的模值的模值的模值的模值的模值数据, 的模值的模值的模值的模值的模制的模值的模制的模值的模值的模值的模值的模值显示的模值的模值的模值的模值的模值的模制的模值的模值的模值的模值的模值的模值的模值的模值的模值的模值的模值的模值的模值值值值值值值值值的模值的模值的模值值值值值的模值的模值的模值的模值的模值的模值的模值的模值的模值的模值值值值值值值的模值的模值的模值的模值的模值