Domain-knowledge Inspired Pseudo Supervision (DIPS) for unsupervised image-to-image translation models to support cross-domain classification

The ability to classify images is dependent on having access to large labeled datasets and testing on data from the same domain of which the model was trained 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 Gaussian Mixture Models and domain knowledge 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 Fréchet Inception Distance (FID) which were designed to evaluate the model in terms of the quality of the generated image from a human-eye perspective. DIPS outperforms state-of-the-art GAN evaluation metrics when selecting the optimal saved checkpoint. 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 boiling crisis problem has been approached as a case study. The code and data to replicate the results can be found on the official GitHub-repository1.