ArXiv

Coordinated Disentanglement with Iterative Mode Discovery Under Hidden Correlations

2026-07-19 · 14:08 UTC ·Rong Hu, Ling Chen ·1 min read
Authors
Rong Hu, Ling Chen
Categories
cs.AI
arXiv
https://arxiv.org/abs/2607.17264v1
PDF
https://arxiv.org/pdf/2607.17264v1

Brief

Based on the abstract, CoDID tackles hidden correlations in disentangled representation learning by jointly discovering modes and enforcing mode-wise conditional independence. The end-to-end framework features a dynamic architecture that grows with the number of discovered modes and a meta-optimization coordination step to prevent error amplification during iteration. Authors report state-of-the-art empirical results on diverse tasks; full text not reviewed.

Why it matters

CoDID (Coordinated Disentanglement with Iterative mode Discovery) jointly discovers latent modes and enforces mode-based conditional independence to handle "hidden correlations" where an attribute value contains multiple underlying modes correlated with other attributes.

Key details

  • CoDID uses a dynamic architecture that adapts to an evolving number of modes and a coordination mechanism based on meta-optimization to mitigate error amplification from iterative updates; authors Rong Hu and Ling Chen report state-of-the-art performance (ICML 2026; arXiv:2607.17264v1, posted 2026-07-19).
Source evidence

Abstract

Disentangled representation learning is a powerful paradigm for robust attribute prediction. While recent methods address attribute correlations, hidden correlations remain underexplored, where data under the value of a certain attribute exhibit underlying modes correlated with other attributes. To preserve mode information and achieve disentanglement, we jointly discover modes and enforce mode-based conditional independence. Yet, the interdependency between these two modules may lead to error amplification under naive iterations. We propose Coordinated Disentanglement with Iterative mode Discovery (CoDID), an end-to-end framework featuring a dynamic architecture that adapts to evolving number of modes, and a coordination mechanism that mitigates error amplification via meta-optimization. Empirical results demonstrate the state-of-the-art performance on diverse tasks.

Comment: Published at ICML 2026