Summary of Hoin: High-order Implicit Neural Representations, by Yang Chen et al.
HOIN: High-Order Implicit Neural Representations
by Yang Chen, Ruituo Wu, Yipeng Liu, Ce Zhu
First submitted to arxiv on: 23 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a universal framework called High-Order Implicit Neural Representations (HOIN) to address the issue of worsening spectral bias in implicit neural representations. HOIN refines the traditional cascade structure by fostering high-order interactions among features, enhancing expressive power and mitigating spectral bias through its neural tangent kernel’s strong diagonal properties. This leads to accelerated and optimized inverse problem resolution. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new framework for processing inverse problems called High-Order Implicit Neural Representations (HOIN). HOIN improves upon traditional methods by fostering high-order interactions among features, making it more effective at solving inverse problems. The approach achieves state-of-the-art recovery quality and training efficiency, making it a promising new direction in the field. |
