📝 SummarXiv

Abstract

Variational Autoencoders (VAEs) with global priors mirror the training set's class frequency in latent space, underrepresenting tail classes and reducing generative fairness on imbalanced datasets. While $t^3$VAE improves robustness via heavy-tailed Student's t-distribution priors, it still allocates latent volume proportionally to the class frequency.In this work, we address this issue by explicitly enforcing equitable latent space allocation across classes. To this end, we propose Conditional-$t^3$VAE, which defines a per-class \mbox{Student's t} joint prior over latent and output variables, preventing dominance by majority classes. Our model is optimized using a closed-form objective derived from the $\gamma$-power divergence. Moreover, for class-balanced generation, we derive an equal-weight latent mixture of Student's t-distributions. On SVHN-LT, CIFAR100-LT, and CelebA, Conditional-$t^3$VAE consistently achieves lower FID scores than both $t^3$VAE and Gaussian-based VAE baselines, particularly under severe class imbalance. In per-class F1 evaluations, Conditional-$t^3$VAE also outperforms the conditional Gaussian VAE across all highly imbalanced settings. While Gaussian-based models remain competitive under mild imbalance ratio ($\rho \lesssim 3$), our approach substantially improves generative fairness and diversity in more extreme regimes.