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Humanoid-inspired Causal Representation Learning for Domain Generalization

Ze Tao, Jian Zhang, Haowei Li, Xianshuai Li, Yifei Peng, Xiyao Liu, Senzhang Wang, Chao Liu, Sheng Ren, Shichao Zhang

TL;DR

The paper tackles domain generalization by introducing the Humanoid-inspired Structural Causal Model (HSCM), which mimics human visual processing to learn fine-grained causal mechanisms. It decomposes images into color, texture, and shape representations via targeted extractors, applies causal interventions and adaptive data transformations, and optimizes a reweighted ELBO-based DAG discovery objective to identify invariant causal structure between source and target domains. The approach is theoretically grounded, with Theorems 1 and 2 guaranteeing classifier minimax optimality and identifiability of the causal graph, and empirically validated on diverse DG benchmarks where HSCM consistently outperforms state-of-the-art baselines and offers interpretable visualizations (CAMs, t-SNE) of its factorized decisions. The work advances robust, interpretable domain generalization by linking causal inference with humanoid-inspired, hierarchical feature processing, and suggests directions for future improvements in flexibility and efficiency.$

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.

Humanoid-inspired Causal Representation Learning for Domain Generalization

TL;DR

The paper tackles domain generalization by introducing the Humanoid-inspired Structural Causal Model (HSCM), which mimics human visual processing to learn fine-grained causal mechanisms. It decomposes images into color, texture, and shape representations via targeted extractors, applies causal interventions and adaptive data transformations, and optimizes a reweighted ELBO-based DAG discovery objective to identify invariant causal structure between source and target domains. The approach is theoretically grounded, with Theorems 1 and 2 guaranteeing classifier minimax optimality and identifiability of the causal graph, and empirically validated on diverse DG benchmarks where HSCM consistently outperforms state-of-the-art baselines and offers interpretable visualizations (CAMs, t-SNE) of its factorized decisions. The work advances robust, interpretable domain generalization by linking causal inference with humanoid-inspired, hierarchical feature processing, and suggests directions for future improvements in flexibility and efficiency.$

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.
Paper Structure (18 sections, 41 equations, 7 figures, 7 tables)

This paper contains 18 sections, 41 equations, 7 figures, 7 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Humanoid-inspired learning
  4. Domain Generalization
  5. Causal Inference for Domain Generalization
  6. Problem and Task Instantiation
  7. Causal View at HSCM Task
  8. Causal Intervention via HSCM
  9. HSCM Task Instantiations
  10. Experiments
  11. Datasets and Setup
  12. Results on Single Domain Generalization
  13. Results on Multiple Domain Generalization
  14. Interpretabilities on Model Decisions
  15. Ablation Studies
  16. ...and 3 more sections

Figures (7)

  • Figure 1: A toy example in a classification model distinguishing between natural wild and artificial zoos. Prior research often spuriously correlates non-causal factors, which can lead to misleading activities. Our approach seeks to address this issue by counteracting the effects of style-based confounding factors, such as desert and grassland’s color, which are often co-occurring with ground-truth like camels, cows, and sheep in the wild.
  • Figure 2: Structure causal graph of Humanoid-inspired Structural Causal Model (HSCM).
  • Figure 3: The proposed HSCM is outlined as: (a) Inspired by the iterative and shared processing of visual information in the cortical regions, we designed the HSCM framework as shown in (b)-(c). This framework sequentially extracts and analyzes visual attributes such as color, texture, and shape to simulate the human brain's visual processing mechanism.
  • Figure 4: Interpretability visualization for CIFAR-10 and CIFAR-10-C classes.
  • Figure 5: Interpretability visualization for PACS classes.
  • ...and 2 more figures