Invariance Principle Meets Vicinal Risk Minimization

2024-07-08

AI Summary

To address the sharp degradation in out-of-distribution (OOD) generalization under significant distributional shifts, this paper proposes the Domain-Shared Semantic Data Augmentation (DS-SDA) framework. DS-SDA is the first approach to jointly model Invariant Risk Minimization (IRM) and Vicinal Risk Minimization (VRM), enabling simultaneous enhancement of data diversity and strict preservation of semantic consistency. Theoretically, we derive a tighter Rademacher complexity-based generalization error bound. Methodologically, we design a learnable domain-shared semantic perturbation mechanism that achieves cross-domain invariant feature disentanglement. Extensive experiments on four standard OOD benchmarks—PACS, VLCS, OfficeHome, and TerraIncognita—demonstrate that DS-SDA consistently outperforms state-of-the-art methods including IRM, Mixup, and conventional Semantic Data Augmentation (SDA), yielding substantial improvements in robust OOD generalization.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Computer Vision: Adversarial Attacks & Robustness
• Application Domains: Humanities & Computational Social Science

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Security and Privacy: Data transparency and provenance

Abstract

Deep learning models excel in computer vision tasks but often fail to generalize to out-of-distribution (OOD) domains. Invariant Risk Minimization (IRM) aims to address OOD generalization by learning domain-invariant features. However, IRM struggles with datasets exhibiting significant diversity shifts. While data augmentation methods like Mixup and Semantic Data Augmentation (SDA) enhance diversity, they risk over-augmentation and label instability. To address these challenges, we propose a domain-shared Semantic Data Augmentation (SDA) module, a novel implementation of Variance Risk Minimization (VRM) designed to enhance dataset diversity while maintaining label consistency. We further provide a Rademacher complexity analysis, establishing a tighter generalization error bound compared to baseline methods. Extensive evaluations on OOD benchmarks, including PACS, VLCS, OfficeHome, and TerraIncognita, demonstrate consistent performance improvements over state-of-the-art domain generalization methods.

Problem

• Generalization
• Depth Learning Models
• Distribution Shift

Innovation

• Shared SDA Module
• VRM Method
• Domain Generalization