通过熵排序实现可灵活压缩的无监督表征解耦。
From Core to Detail: Unsupervised Disentanglement with Entropy-Ordered Flows
- 按解释熵排序潜在变量，形成可动态选择核心表示的流模型。
- 在CelebA上实现高压缩率与强去噪能力，保留语义特征。
- 适合需要可控表征复杂度的图像生成与分析任务。
学习既具语义意义又跨训练稳定无监督表征，仍是现代表示学习的核心挑战。我们提出熵排序流（EOFlows），一种基于归一化流的框架，将潜在维度按其解释熵排序，类比于PCA的解释方差。该排序使能自适应注入流：训练后可仅保留前C个潜在变量构成紧凑核心表示，其余变量捕捉细粒度细节与噪声，其中C可在推理时灵活选择而非训练时固定。EOFlows融合独立机制分析、主成分流与流形熵度量的洞见。我们结合基于似然的训练、局部雅可比正则化和噪声增强，使方法可扩展至高维数据如图像。在CelebA数据集上的实验表明，该方法揭示出丰富语义可解释特征，支持高压缩与强去噪能力。
原文摘要 · Abstract (English)
Learning unsupervised representations that are both semantically meaningful and stable across runs remains a central challenge in modern representation learning. We introduce entropy-ordered flows (EOFlows), a normalizing-flow framework that orders latent dimensions by their explained entropy, analogously to PCA's explained variance. This ordering enables adaptive injective flows: after training, one may retain only the top C latent variables to form a compact core representation while the remaining variables capture fine-grained detail and noise, with C chosen flexibly at inference time rather than fixed during training. EOFlows build on insights from Independent Mechanism Analysis, Principal Component Flows and Manifold Entropic Metrics. We combine likelihood-based training with local Jacobian regularization and noise augmentation into a method that scales well to high-dimensional data such as images. Experiments on the CelebA dataset show that our method uncovers a rich set of semantically interpretable features, allowing for high compression and strong denoising.
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