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Improving the performance on an imbalanced training set is one of the mainchallenges in nowadays Machine Learning. One way to augment and thus re-balancethe image dataset is through existing deep generative models, likeclass-conditional Generative Adversarial Networks (cGAN) or Diffusion Models bysynthesizing images on each of the tail-class. Our experiments on imbalancedimage dataset classification show that, the validation accuracy improvementwith such re-balancing method is related to the image similarity betweendifferent classes. Thus, to quantify this image dataset class similarity, wepropose a measurement called Super-Sub Class Structural Similarity(SSIM-supSubCls) based on Structural Similarity (SSIM). A deep generative modeldata augmentation classification (GM-augCls) pipeline is also provided toverify this metric correlates with the accuracy enhancement. We furtherquantify the relationship between them, discovering that the accuracyimprovement decays exponentially with respect to SSIM-supSubCls values.

Structural Similarity: When to Use Deep Generative Models on Imbalanced Image Dataset Augmentation