Conditioning Tabular Diffusion Models on Class Statistics versus Instance Constraints for Minority Class Performance

Abstract

Abstract: This survey explores recent advances in addressing class imbalance issues for developing machine learning models in precision agriculture, with a focus on techniques used for plant disease detection, soil management, and crop classification. We examine the impact of class imbalance on agricultural data and evaluate various resampling methods, such as oversampling and undersampling, as well as algorithm-level approaches, to mitigate this challenge. The paper also highlights the importance of evaluation metrics, including F1-score, G-mean, and MCC, in assessing the performance of machine learnin

Research Question

What is the impact of conditioning tabular diffusion models on class-specific statistics versus instance-level constraints on the FID score and downstream task accuracy for minority classes?

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Correction Record

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Provenance