Title: Disentangled Information Quantification for Dataset Construction in Data-Centric AI
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: Data-Centric AI; Disentangled representation learning; Active learning; Sample information quantification

Abstract: The rise of large-scale models has placed greater emphasis on Data-Centric AI (DCAI), which aims to build high-quality, scalable, and sustainable data assets. However, prevailing dataset construction relies on active learning with model-dependent criteria (e.g., uncertainty or confidence), typically anchored to a specific evaluation model. This coupling ties sample selection to architecture- and training-specific behavior, undermining transferability and reusability. We introduce Disentangled Generalizable Construction (DGC), a model-agnostic data selection method that, under a fixed sample budget, maximizes semantic coverage by selecting subsets that faithfully represent the dataset’s key semantic factors, thereby preserving utility across models and training settings. As a supporting module, the Orthogonal Factorization Soft-Quantization Autoencoder (OF-SQAE) yields stable, interpretable semantic factors; Invariant Attribute Information Quantification (IAIQ) quantifies per-sample informativeness in the latent space; and a coverage-driven greedy algorithm selects representative samples. Experiments on natural-image datasets and evaluations across diverse model architectures show strong results, indicating the generality and robustness of DGC.

TL;DR: Model-agnostic DGC quantifies each sample’s semantic information in a disentangled space and maximizes coverage to select a highly generalizable dataset with minimal labeled data.