OpenViewer: Openness-Aware Multi-View Learning

Abstract: Multi-view learning methods leverage multiple data sources to enhance perception by mining correlations across views, typically relying on predefined categories. However, deploying these models in real-world scenarios presents two primary openness challenges. 1) Lack of Interpretability: The integration mechanisms of multi-view data in existing black-box models remain poorly explained; 2) Insufficient Generalization: Most models are not adapted to multi-view scenarios involving unknown categories. To address these challenges, we propose OpenViewer, an openness-aware multi-view learning framework with theoretical support. This framework begins with a Pseudo-Unknown Sample Generation Mechanism to efficiently simulate open multi-view environments and previously adapt to potential unknown samples. Subsequently, we introduce an Expression-Enhanced Deep Unfolding Network to intuitively promote interpretability by systematically constructing functional prior-mapping modules and effectively providing a more transparent integration mechanism for multi-view data. Additionally, we establish a Perception-Augmented Open-Set Training Regime to significantly enhance generalization by precisely boosting confidences for known categories and carefully suppressing inappropriate confidences for unknown ones. Experimental results demonstrate that OpenViewer effectively addresses openness challenges while ensuring recognition performance for both known and unknown samples. The code is released at https://github.com/dushide/OpenViewer.

• The paper presents OpenViewer, a framework addressing multi-view learning challenges (interpretability, generalization) for unknown categories via novel mechanisms.
• OpenViewer uses pseudo-unknown sample generation, an expression-enhanced deep unfolding network, and a perception-augmented open-set training regime.
• Theoretical analysis and empirical validation show OpenViewer significantly improves performance and robustness in distinguishing known from unknown categories.

OpenViewer: Openness-Aware Multi-View Learning

The paper presents OpenViewer, a framework that addresses the challenges of openness in multi-view learning by improving interpretability and generalization. Conventional multi-view learning approaches often assume known category samples, limiting their applicability in open-world scenarios. This study highlights two main challenges: the lack of interpretability in multi-view data integration and insufficient generalization when exposed to unknown categories. To overcome these challenges, OpenViewer is introduced as an openness-aware multi-view learning framework, substantiated by both theoretical and empirical analysis.

Key Contributions and Innovations

Openness Challenges in Multi-View Learning:

OpenViewer targets two principal challenges in multi-view learning:

1. Lack of Interpretability: The opacity of traditional models in handling multi-view data integration.
2. Insufficient Generalization: The inability of existing methods to adapt to unknown categories, often leading to misclassification with high confidence.

Proposed Framework:

The formulation of OpenViewer involves several novel mechanisms:

• Pseudo-Unknown Sample Generation: This mechanism simulates open environments and aids models in adapting to potential unknown samples. It involves generating pseudo-unknown samples by perturbing original samples using a sampled parameter from a beta distribution.
• Expression-Enhanced Deep Unfolding Network: This network is designed to enhance interpretability, incorporating functional prior-mapping modules such as redundancy removal, dictionary learning, noise processing, and complementarity fusion.
• Perception-Augmented Open-Set Training Regime: This training regime aims to improve generalization by enhancing the confidence of known categories while suppressing inappropriate confidence in unknown categories.

Theoretical and Empirical Support

Theoretical Analysis:

The framework provides a theoretical foundation that ensures the interpretability and generalization capabilities of OpenViewer. It includes:

• Convergence guarantees for the network modules ensuring improved interpretability.
• A bounded solution space for consistency during multi-view data integration.
• Stability in generalization, supported by convergence rates and radius under specific conditions.

Empirical Validation:

Comprehensive experiments on diverse real-world datasets validate OpenViewer's effectiveness. The results demonstrated significant improvements in recognition performance over existing methods, especially in distinguishing known from unknown categories. The OSCR curve results and CCR at different FPR levels showed OpenViewer achieving superior classification results in all tested conditions.

Implications and Future Directions

OpenViewer presents substantial contributions to the field of multi-view learning, particularly in open-world scenarios. By directly addressing challenges of interpretability and generalization, it provides a robust framework capable of handling unknown categories in real-world applications. This work suggests a shift towards models that not only recognize classes more effectively but also offer insights into their decision-making processes.

The potential for future research is broad. Extensions of OpenViewer could involve incorporating more sophisticated simulator mechanisms for unknown categories and exploring its applicability to other multi-modal datasets beyond those tested. The approach can also motivate advancements in handling incomplete or heterogeneous data sources, further extending the framework's utility across various AI applications.