The ability to classify images is dependent on having access to large labeled datasets and testing on data from the same domain that the model can train on. Classification becomes more challenging when dealing with new data from a different domain, where gathering and especially labeling a larger image dataset for retraining a classification model requires a labor-intensive human effort. Cross-domain classification frameworks were developed to handle this data domain shift problem by utilizing unsupervised image-to-image translation models to translate an input image from the unlabeled domain to the labeled domain. The problem with these unsupervised models lies in their unsupervised nature. For lack of annotations, it is not possible to use the traditional supervised metrics to evaluate these translation models to pick the best-saved checkpoint model. This paper introduces a new method called Domain-knowledge Inspired Pseudo Supervision (DIPS) which utilizes domain-informed Gaussian Mixture Models to generate pseudo annotations to enable the use of traditional supervised metrics. This method was designed specifically to support cross-domain classification applications contrary to other typically used metrics such as the FID which were designed to evaluate the model in terms of the quality of the generated image from a human-eye perspective. DIPS proves its effectiveness by outperforming various GAN evaluation metrics, including FID, when selecting the optimal saved checkpoint model. It is also evaluated against truly supervised metrics. Furthermore, DIPS showcases its robustness and interpretability by demonstrating a strong correlation with truly supervised metrics, highlighting its superiority over existing state-of-the-art alternatives. The code and data to replicate the results can be found on the official Github repository: https://github.com/Hindawi91/DIPS

翻译：图像分类的能力依赖于大规模标注数据集的获取以及模型训练时对同域测试数据的可用性。当处理来自不同领域的新数据时，分类任务变得更具挑战性，因为收集尤其是标注大规模图像数据集以重新训练分类模型需要大量人力劳动。跨域分类框架通过利用无监督图像到图像翻译模型，将未标注领域的输入图像转换至已标注领域，从而解决数据领域偏移问题。然而，这类无监督模型的缺陷在于其无监督特性：由于缺乏标注，无法使用传统监督指标评估这些翻译模型以选择最优保存的检查点模型。本文提出一种名为“领域知识启发的伪监督方法（DIPS）”的新技术，该方法利用领域信息引导的高斯混合模型生成伪标注，从而启用传统监督指标。与FID等常用评估指标（旨在从人眼视角评估生成图像质量）不同，DIPS专为支持跨域分类应用而设计。在选择最优保存检查点模型时，DIPS通过优于包括FID在内的多种GAN评估指标证明了其有效性，并与真实监督指标进行了对比评估。此外，DIPS通过展示与真实监督指标的强相关性，凸显了其鲁棒性和可解释性，彰显出相较于现有最优替代方案的优越性。可复现结果的代码与数据已发布在官方GitHub仓库：https://github.com/Hindawi91/DIPS