mlsanityData to 14 Oct 2026

T-MLP: Tailed Multi-Layer Perceptron for Level-of-Detail Signal Representation

arXiv 2509.00066 ↗First posted 26 Aug 2025cs.LG cs.GR eess.IVcited by 0

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Abstract (arXiv)

Level-of-detail (LoD) representation is critical for efficiently modeling and transmitting various types of signals, such as images and 3D shapes. In this work, we propose a novel network architecture that enables LoD signal representation. Our approach builds on a modified Multi-Layer Perceptron (MLP), which inherently operates at a single scale and thus lacks native LoD support. Specifically, we introduce the Tailed Multi-Layer Perceptron (T-MLP), which extends the MLP by attaching an output branch, also called tail, to each hidden layer. Each tail refines the residual between the current prediction and the ground-truth signal, so that the accumulated outputs across layers correspond to the target signals at different LoDs, enabling multi-scale modeling with supervision from only a single-resolution signal. Extensive experiments demonstrate that our T-MLP outperforms existing neural LoD baselines across diverse signal representation tasks.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 40 of its 58 references that are in the map. First posted on arXiv 26 Aug 2025.

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References · 40

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28 more
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