IndiSeek learns information-guided disentangled representations

• URL: http://arxiv.org/abs/2509.21584v2
• Date: Sat, 25 Oct 2025 13:49:31 GMT
• Title: IndiSeek learns information-guided disentangled representations
• Authors: Yu Gui, Cong Ma, Zongming Ma,
• Abstract summary: We introduce IndiSeek, a novel disentangled representation learning approach.<n>IndiSeek balances independence and completeness, enabling principled extraction of modality-specific features.<n>We demonstrate the effectiveness of IndiSeek on synthetic simulations, a CITE-seq dataset and multiple real-world multi-modal benchmarks.
• Score: 9.021908698915013
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Learning disentangled representations is a fundamental task in multi-modal learning. In modern applications such as single-cell multi-omics, both shared and modality-specific features are critical for characterizing cell states and supporting downstream analyses. Ideally, modality-specific features should be independent of shared ones while also capturing all complementary information within each modality. This tradeoff is naturally expressed through information-theoretic criteria, but mutual-information-based objectives are difficult to estimate reliably, and their variational surrogates often underperform in practice. In this paper, we introduce IndiSeek, a novel disentangled representation learning approach that addresses this challenge by combining an independence-enforcing objective with a computationally efficient reconstruction loss that bounds conditional mutual information. This formulation explicitly balances independence and completeness, enabling principled extraction of modality-specific features. We demonstrate the effectiveness of IndiSeek on synthetic simulations, a CITE-seq dataset and multiple real-world multi-modal benchmarks.

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