Summary of D’oh: Decoder-only Random Hypernetworks For Implicit Neural Representations, by Cameron Gordon et al.
D’OH: Decoder-Only Random Hypernetworks for Implicit Neural Representations
by Cameron Gordon, Lachlan Ewen MacDonald, Hemanth Saratchandran, Simon Lucey
First submitted to arxiv on: 28 Mar 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 proposed paper explores the idea of leveraging cross-layer redundancies in deep implicit functions to achieve additional compression. The authors suggest using a novel runtime decoder-only hypernetwork, called D’OH, which does not require offline training data. This approach aims to optimize the memory footprint of neural representations without the need for architecture search or large datasets. By directly adjusting the latent code dimension, the paper provides a natural way to vary the representation’s memory requirements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep learning researchers are working on new ways to compress signals like images and sounds into smaller digital packages. One approach is called deep implicit functions. These functions can store lots of information in just a few numbers. The idea is that different parts of these signals might be repeating themselves, so if we can find those repeats, we can get rid of some of the extra data. In this paper, scientists suggest a new way to find and use those repeats. They propose a special computer program called D’OH (Decoder-Only randomly projected Hypernetwork) that does not need a lot of training data beforehand. This program can help make digital signals smaller without needing huge amounts of information. |
Keywords
* Artificial intelligence * Decoder * Deep learning
