Summary of Inductive Gradient Adjustment For Spectral Bias in Implicit Neural Representations, by Kexuan Shi et al.
Inductive Gradient Adjustment For Spectral Bias In Implicit Neural Representations
by Kexuan Shi, Hai Chen, Leheng Zhang, Shuhang Gu
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 explores Implicit Neural Representations (INRs) in computer vision tasks, which are typically achieved through complex MLP architectures or training techniques. However, this study delves into the linear dynamics model of MLPs and identifies the empirical Neural Tangent Kernel (eNTK) matrix as a reliable link between spectral bias and training dynamics. The authors propose an inductive gradient adjustment method based on eNTK, which improves the spectral bias via inductive generalization of eNTK-based gradient transformation matrices. The method is evaluated on various INRs tasks with different architectures and compared to existing training techniques, demonstrating superior representation performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to make computer programs better at understanding pictures. It uses a special way called Implicit Neural Representations (INRs) which helps the program learn from pictures. The problem is that these programs need very complicated designs or special ways of learning to work well. This study figures out why this happens and finds a new way to make the programs better by changing how they learn. They test this method on different types of picture understanding tasks and show that it works better than other methods. |
Keywords
» Artificial intelligence » Generalization
