OpenEnhancer: An open-set learning network via multiple feature fusion

Multiple feature fusion networks integrate diverse features to enhance predictive performance, yet they face two key challenges that require further exploration. First, most studies focus on feature fusion within known closed-set classes, overlooking the more practically significant open-set scenario with unknown classes. Second, these approaches often encounter adaptability conflicts, including expressivity limitations from inaccurate patterns captured in handcrafted features, overadapting due to excessive adaptation to the training data in deep features, and inconsistent feature hierarchy challenges in mixed-feature scenarios. To address these challenges, we propose OpenEnhancer to improve open-set learning through multiple feature fusion. We introduce a pseudo-unknown mechanism and a feature-specific sparse learning layer to form an enhanced feature-specific sparse module that better adapts the model to open-set scenarios and mitigates adaptability conflicts like expressivity limitations and overadapting. The processed features are integrated using a multi-feature attention fusion module, which further alleviates the challenges posed by inconsistent feature hierarchies. Finally, we employ an open-set loss regime to reduce spurious confidence in known classes when the model is exposed to the unknown, thereby improving recognition accuracy. Open-set simulation experiments on multi-feature datasets demonstrate that OpenEnhancer not only achieves strong recognition performance but also effectively mitigates adaptability conflicts. The datasets and source code are publicly available to ensure reproducibility: https://github.com/dushide/OpenEnhancer.