Summary of Preconditioners For the Stochastic Training Of Implicit Neural Representations, by Shin-fang Chng et al.
Preconditioners for the Stochastic Training of Implicit Neural Representations
by Shin-Fang Chng, Hemanth Saratchandran, Simon Lucey
First submitted to arxiv on: 13 Feb 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 proposed paper explores alternative optimization techniques to accelerate the training process of implicit neural representations without sacrificing accuracy. These neural networks are powerful tools for encoding complex signals in computer vision, robotics, and geometry, but traditional methods like Adam require lengthy training times. The authors propose using curvature-aware diagonal preconditioners for stochastic training, demonstrating their effectiveness across various signal modalities, including images, shape reconstruction, and Neural Radiance Fields (NeRF). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making it faster to train special kinds of computer networks that can help with things like recognizing shapes or pictures. These networks are really good at understanding complex information, but they take a long time to learn. The researchers found a new way to make them learn faster without losing accuracy. They tested this method on different types of data and showed it works well for things like recognizing objects in images or reconstructing 3D shapes. |
Keywords
* Artificial intelligence * Optimization
