VAE-Inf: A statistically interpretable generative paradigm for imbalanced classification

• URL: http://arxiv.org/abs/2604.25334v1
• Date: Tue, 28 Apr 2026 07:50:56 GMT
• Title: VAE-Inf: A statistically interpretable generative paradigm for imbalanced classification
• Abstract summary: We propose a two-stage framework to bridge the gap between generative modeling and discriminative classification.<n>For inference, we introduce a projection-based score that admits a natural hypothesis testing interpretation.<n>Experiments on diverse real-world benchmarks demonstrate that our framework achieves competitive performance against other approaches.
• Score: 8.677199689027772
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Imbalanced classification remains a pervasive challenge in machine learning, particularly when minority samples are too scarce to provide a robust discriminative boundary. In such extreme scenarios, conventional models often suffer from unstable decision boundaries and a lack of reliable error control. To bridge the gap between generative modeling and discriminative classification, we propose a two-stage framework \textbf{VAE-Inf} that integrates deep representation learning with statistically interpretable hypothesis testing. In the first stage, we adopt a one-class modeling perspective by training a variational autoencoder (VAE) exclusively on majority-class data to capture the underlying reference distribution. The resulting latent posteriors are aggregated via a Wasserstein barycenter to construct a global Gaussian reference model, providing a geometrically principled baseline for the majority class. In the second stage, we transform this generative foundation into a discriminative classifier by fine-tuning the encoder with limited minority samples. This is achieved through a novel distribution-aware loss that enforces probabilistic separation between classes based on variance-normalized projection statistics. For inference, we introduce a projection-based score that admits a natural hypothesis testing interpretation, allowing for a distribution-free calibration procedure. This approach yields exact finite-sample control of the Type-I error (false positive rate) without relying on restrictive parametric assumptions. Extensive experiments on diverse real-world benchmarks demonstrate that our framework achieves competitive performance against other approaches. The codes are available upon request.

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