无监督学习中通过特征分布对齐实现鲁棒表征，无需标签即可捕捉跨环境不变性。
Unsupervised Representation Learning - an Invariant Risk Minimization Perspective
- 基于特征分布对齐重构不变性，摆脱标签依赖
- 提出PICA与VIAE两种方法，分别处理线性与深度生成场景
- 支持条件生成与干预，适合需泛化能力的无监督场景
我们提出一种新型无监督框架，将不变风险最小化（IRM）概念拓展至无标签场景。传统IRM依赖标注数据学习对分布偏移鲁棒的表征，而本方法通过特征分布对齐重新定义不变性，实现无标签下的鲁棒表征学习。框架内引入两种方法：主不变分量分析（PICA），在高斯假设下提取线性不变方向；变分不变自编码器（VIAE），通过深度生成模型分离环境不变与环境依赖的潜在因子。该方法基于新型无监督结构因果模型，支持环境条件下的样本生成与干预。在合成数据集、修改版MNIST及CelebA上的实验表明，所提方法能有效捕捉不变结构，保留相关信息，并在无标签条件下实现跨环境泛化。
原文摘要 · Abstract (English)
We propose a novel unsupervised framework for \emph{Invariant Risk Minimization} (IRM), extending the concept of invariance to settings where labels are unavailable. Traditional IRM methods rely on labeled data to learn representations that are robust to distributional shifts across environments. In contrast, our approach redefines invariance through feature distribution alignment, enabling robust representation learning from unlabeled data. We introduce two methods within this framework: Principal Invariant Component Analysis (PICA), a linear method that extracts invariant directions under Gaussian assumptions, and Variational Invariant Autoencoder (VIAE), a deep generative model that separates environment-invariant and environment-dependent latent factors. Our approach is based on a novel ``unsupervised'' structural causal model and supports environment-conditioned sample-generation and intervention. Empirical evaluations on synthetic dataset, modified versions of MNIST, and CelebA demonstrate the effectiveness of our methods in capturing invariant structure, preserving relevant information, and generalizing across environments without access to labels.
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