Intrinsic Dimensionality as a Model-Free Measure of Class Imbalance

The exploration of class imbalance in machine learning, as presented in the paper on Intrinsic Dimensionality (ID), aligns with ongoing research in the field, such as the study on robust fine-tuning of vision-language models. Both works emphasize the importance of effective metrics and methodologies in enhancing model performance. The ID approach not only simplifies the measurement of imbalance but also complements existing strategies, as seen in the context of contrastive pre-trained models. This synergy highlights a broader trend in AI research focusing on improving classification accuracy through innovative metrics.