Humanoid-inspired Causal Representation Learning for Domain Generalization

2025-10-18

AI Summary

Traditional domain generalization (DG) methods rely on statistical correlations, failing to capture causal dependencies between data and labels, and thus suffer from limited generalizability and interpretability in dynamic, complex environments. To address this, we propose the Human-like Structural Causal Model (HSCM), the first DG framework to incorporate the hierarchical processing mechanism of the human visual system into causal representation learning. HSCM achieves fine-grained disentanglement of image attributes—color, texture, and shape—and integrates dynamic causal reweighting to learn domain-invariant representations grounded in causal invariance. By unifying structural causal models (SCMs), causal representation learning, and hierarchical neural networks, HSCM theoretically guarantees causal invariance. Empirically, it achieves state-of-the-art performance across multiple standard DG benchmarks, demonstrating superior generalizability, robustness, and interpretability. The implementation is publicly available.

Technology Category

• Machine Learning: Causal Learning
• Computer Vision: Visual Reasoning & Symbolic Representations
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

Abstract

This paper proposes the Humanoid-inspired Structural Causal Model (HSCM), a novel causal framework inspired by human intelligence, designed to overcome the limitations of conventional domain generalization models. Unlike approaches that rely on statistics to capture data-label dependencies and learn distortion-invariant representations, HSCM replicates the hierarchical processing and multi-level learning of human vision systems, focusing on modeling fine-grained causal mechanisms. By disentangling and reweighting key image attributes such as color, texture, and shape, HSCM enhances generalization across diverse domains, ensuring robust performance and interpretability. Leveraging the flexibility and adaptability of human intelligence, our approach enables more effective transfer and learning in dynamic, complex environments. Through both theoretical and empirical evaluations, we demonstrate that HSCM outperforms existing domain generalization models, providing a more principled method for capturing causal relationships and improving model robustness. The code is available at https://github.com/lambett/HSCM.

Problem

• Proposes a human-inspired causal model for domain generalization
• Disentangles image attributes like color, texture, and shape
• Enhances generalization and robustness across diverse domains

Innovation

• Humanoid-inspired causal framework for domain generalization
• Disentangles and reweights image attributes like color and shape
• Models hierarchical processing of human vision systems