A Deep Positive-Negative Prototype Approach to Integrated Prototypical Discriminative Learning

2025-01-05

AI Summary

To address the challenge of simultaneously achieving high accuracy and interpretability in deep learning classification models on complex data, this paper proposes the Deep Positive-Negative Prototype (DPNP) model, which unifies class prototypes with classification weights to construct discriminative and interpretable feature representations in the latent space. Methodologically, DPNP introduces a novel positive-negative prototype collaboration mechanism: each class is represented by an intra-class positive prototype, while inter-class positive prototypes implicitly serve as parameter-free negative prototypes, exerting repulsive forces that significantly enhance inter-class separation. The model jointly optimizes pretrained feature extraction, prototype alignment, and separation via a new composite loss function integrating cross-entropy, alignment, and separation terms. Experiments demonstrate that DPNP surpasses state-of-the-art methods across multiple benchmark datasets, supports lightweight network architectures, and yields prototypes that exhibit near-regular geometric arrangements in low-dimensional space—substantially improving intra-class compactness and inter-class margin.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Computer Vision: Representation Learning for Vision

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

This paper proposes a novel Deep Positive-Negative Prototype (DPNP) model that combines prototype-based learning (PbL) with discriminative methods to improve class compactness and separability in deep neural networks. While PbL traditionally emphasizes interpretability by classifying samples based on their similarity to representative prototypes, it struggles with creating optimal decision boundaries in complex scenarios. Conversely, discriminative methods effectively separate classes but often lack intuitive interpretability. Toward exploiting advantages of these two approaches, the suggested DPNP model bridges between them by unifying class prototypes with weight vectors, thereby establishing a structured latent space that enables accurate classification using interpretable prototypes alongside a properly learned feature representation. Based on this central idea of unified prototype-weight representation, Deep Positive Prototype (DPP) is formed in the latent space as a representative for each class using off-the-shelf deep networks as feature extractors. Then, rival neighboring class DPPs are treated as implicit negative prototypes with repulsive force in DPNP, which push away DPPs from each other. This helps to enhance inter-class separation without the need for any extra parameters. Hence, through a novel loss function that integrates cross-entropy, prototype alignment, and separation terms, DPNP achieves well-organized feature space geometry, maximizing intra-class compactness and inter-class margins. We show that DPNP can organize prototypes in nearly regular positions within feature space, such that it is possible to achieve competitive classification accuracy even in much lower-dimensional feature spaces. Experimental results on several datasets demonstrate that DPNP outperforms state-of-the-art models, while using smaller networks.

Problem

• Deep Learning
• Interpretable Classification
• Complex Datasets

Innovation

• Deep Positive and Negative Prototypes (DPNP)
• Discriminative Prototype Learning
• Enhanced Classification Performance