Generalized Deep internal Learning For Hyperspectral Image Super Resolution

The class-imbalance issue is intrinsic to many real-world machine learning tasks, especially the rare-event classification problems. Although the impact and treatment of imbalanced data is widely known, the magnitude of a metric's sensitivity to class imbalance is not quantified yet. As a result, often the sensitive metrics are dismissed while their sensitivity may only be marginal. in this paper, we introduce an intuitive evaluation framework that quantifies metrics? sensitivity to the class imbalance. Moreover, we reveal an interesting fact that there is a logarithmic behavior in metrics? sensitivity meaning that the higher imbalance ratios are associated with the lower sensitivity of metrics. Our framework builds an intuitive understanding of the class-imbalance impact on metrics. We believe this can help avoid many common mistakes, specially the less-emphasized and incorrect assumption that all metrics? quantities are comparable under different class-imbalance ratios.