Summary of Towards Croppable Implicit Neural Representations, by Maor Ashkenazi et al.
Towards Croppable Implicit Neural Representations
by Maor Ashkenazi, Eran Treister
First submitted to arxiv on: 28 Sep 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 the idea of editable Implicit Neural Representations (INRs) and specifically focuses on the widely used cropping operation. The authors present Local-Global SIRENs, a novel INR architecture that supports cropping by design. This architecture combines local and global feature extraction for signal encoding, allowing effortless removal of specific portions of an encoded signal with proportional weight decrease. The design eliminates the need for retraining, making it suitable for straightforward extension of previously encoded signals. Beyond signal editing, the Local-Global approach accelerates training, enhances encoding of various signals, improves downstream performance, and can be applied to modern INRs like INCODE, highlighting its potential and flexibility. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making neural networks that can represent natural signals in a way that’s easy to edit. It’s hard to change these neural networks after they’re trained because they’re “black boxes”. The authors create a new kind of neural network called Local-Global SIRENs that lets you remove parts of the signal without retraining the whole thing. This makes it easier to add or change things in the signal. They also show how this new approach can make training faster, help encode different types of signals better, and work with other kinds of neural networks. |
Keywords
» Artificial intelligence » Feature extraction » Neural network
