  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
