Summary of Nonparametric Teaching Of Implicit Neural Representations, by Chen Zhang et al.
Nonparametric Teaching of Implicit Neural Representations
by Chen Zhang, Steven Tin Sui Luo, Jason Chun Lok Li, Yik-Chung Wu, Ngai Wong
First submitted to arxiv on: 17 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 investigates the learning of implicit neural representations (INRs) using an overparameterized multilayer perceptron (MLP). A novel nonparametric teaching perspective is proposed to efficiently select examples for teaching target functions, such as image functions defined by 2D grids. The Implicit Neural Teaching (INT) paradigm treats INR learning as a nonparametric teaching problem, selecting signal fragments for iterative training of the MLP to achieve fast convergence. By connecting MLP evolution through parameter-based gradient descent and function evolution through functional gradient descent in nonparametric teaching, the paper shows that teaching an overparameterized MLP is consistent with teaching a nonparametric learner. This discovery enables convenient drop-in of nonparametric teaching algorithms to enhance INR training efficiency by 30%+ across various input modalities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how computers can learn new things without needing a lot of data or examples. They use a special kind of computer program called an overparameterized multilayer perceptron (MLP) and teach it to understand images by showing it parts of those images. This helps the MLP learn faster and do better, which is important because learning from lots of data can take a long time. |
Keywords
» Artificial intelligence » Gradient descent
