Learning with Adaptive Prototype Manifolds for Out-of-Distribution Detection

TL;DR

This paper introduces APEX, a novel framework for out-of-distribution detection that adaptively optimizes class prototypes and bridges learning and inference gaps, achieving state-of-the-art results.

Contribution

The paper proposes APEX, which uses an adaptive prototype manifold and posterior-aware scoring to improve OOD detection by addressing fundamental flaws in existing methods.

Findings

APEX achieves state-of-the-art OOD detection performance on CIFAR-100.

The adaptive prototype manifold effectively resolves prototype collision issues.

The posterior-aware scoring enhances the model's ability to distinguish in- and out-of-distribution samples.

Abstract

Out-of-distribution (OOD) detection is a critical task for the safe deployment of machine learning models in the real world. Existing prototype-based representation learning methods have demonstrated exceptional performance. Specifically, we identify two fundamental flaws that universally constrain these methods: the Static Homogeneity Assumption (fixed representational resources for all classes) and the Learning-Inference Disconnect (discarding rich prototype quality knowledge at inference). These flaws fundamentally limit the model's capacity and performance. To address these issues, we propose APEX (Adaptive Prototype for eXtensive OOD Detection), a novel OOD detection framework designed via a Two-Stage Repair process to optimize the learned feature manifold. APEX introduces two key innovations to address these respective flaws: (1) an Adaptive Prototype Manifold (APM), which leverages the Minimum Description Length (MDL) principle to automatically determine the optimal prototype complexity $K_c^*$ for each class, thereby fundamentally resolving prototype collision; and (2) a Posterior-Aware OOD Scoring (PAOS) mechanism, which quantifies prototype quality (cohesion and separation) to bridge the learning-inference disconnect. Comprehensive experiments on benchmarks such as CIFAR-100 validate the superiority of our method, where APEX achieves new state-of-the-art performance.