Correlation of Novel Tabular Generative Metrics with Multimodal Alignment Robustness Under Numerical Distribution Shifts

Abstract

Abstract: Abstract The existence of class imbalance in a dataset can greatly bias the classifier towards majority classification. This discrepancy can pose a serious problem for deep learning models, which require copious and diverse amounts of data to learn patterns and output classifications. Traditionally, data-level and algorithm-level techniques have been instrumental in mitigating the adverse effect of class imbalance. With the recent development and proliferation of Generative Adversarial Networks (GANs), researchers across a variety of disciplines have adapted the architecture of GANs and implem

Research Question

How do novel tabular generative metrics correlate with the robustness of multimodal alignment under numerical distribution shifts?

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