Escaping Plato's Cave: Robust Conceptual Reasoning through Interpretable 3D Neural Object Volumes

2025-03-17

AI Summary

To address the core challenge of simultaneously ensuring robustness and interpretability for AI models in high-stakes scenarios, this paper proposes CAVE—the first image classification framework integrating 3D-aware robust representation learning with concept-level interpretability. Methodologically, CAVE employs 3D neural voxel modeling to learn physically grounded semantic concepts; it then aligns voxel representations with human-understandable concepts via voxel-concept distillation and analyzes concept activation vectors (CAVs) to enable sample-consistent, visually verifiable, and semantically plausible concept-driven inference. Contributions include: (1) the first unification of 3D geometric robustness with concept-based interpretability; (2) overcoming key limitations of prior black-box concept methods in generalizability and trustworthiness; and (3) achieving state-of-the-art out-of-distribution robustness (on OOD detection and corruption benchmarks) while significantly outperforming existing approaches across multiple quantitative interpretability metrics.

Technology Category

• Computer Vision: Interpretability, Explainability, and Transparency
• Machine Learning: Adversarial Learning & Robustness
• Humans and AI: Explainable AI (XAI) for Human Understanding

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

With the rise of neural networks, especially in high-stakes applications, these networks need two properties (i) robustness and (ii) interpretability to ensure their safety. Recent advances in classifiers with 3D volumetric object representations have demonstrated a greatly enhanced robustness in out-of-distribution data. However, these 3D-aware classifiers have not been studied from the perspective of interpretability. We introduce CAVE - Concept Aware Volumes for Explanations - a new direction that unifies interpretability and robustness in image classification. We design an inherently-interpretable and robust classifier by extending existing 3D-aware classifiers with concepts extracted from their volumetric representations for classification. In an array of quantitative metrics for interpretability, we compare against different concept-based approaches across the explainable AI literature and show that CAVE discovers well-grounded concepts that are used consistently across images, while achieving superior robustness.

Problem

• Enhancing robustness and interpretability in neural networks
• Unifying interpretability with 3D volumetric object representations
• Developing inherently-interpretable and robust image classifiers

Innovation

• 3D volumetric object representations enhance robustness
• CAVE integrates interpretability with robust image classification
• Concepts from volumetric representations improve classifier interpretability