Decomposer Networks: Deep Component Analysis and Synthesis

• URL: http://arxiv.org/abs/2510.09825v1
• Date: Fri, 10 Oct 2025 19:55:13 GMT
• Title: Decomposer Networks: Deep Component Analysis and Synthesis
• Authors: Mohsen Joneidi,
• Abstract summary: We propose a semantic autoencoder that factorizes an input into multiple interpretable components.<n>By unrolling a Gauss--Seidel style block-coordinate descent into a differentiable network, DecompNet enforces explicit competition among components.<n>We highlight its novelty as the first semantic autoencoder to implement an all-but-one residual update rule.
• Score: 0.14504054468850663
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose the Decomposer Networks (DecompNet), a semantic autoencoder that factorizes an input into multiple interpretable components. Unlike classical autoencoders that compress an input into a single latent representation, the Decomposer Network maintains N parallel branches, each assigned a residual input defined as the original signal minus the reconstructions of all other branches. By unrolling a Gauss--Seidel style block-coordinate descent into a differentiable network, DecompNet enforce explicit competition among components, yielding parsimonious, semantically meaningful representations. We situate our model relative to linear decomposition methods (PCA, NMF), deep unrolled optimization, and object-centric architectures (MONet, IODINE, Slot Attention), and highlight its novelty as the first semantic autoencoder to implement an all-but-one residual update rule.

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