Summary of Latent Communication in Artificial Neural Networks, by Luca Moschella
Latent Communication in Artificial Neural Networks
by Luca Moschella
First submitted to arxiv on: 16 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
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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 As a machine learning educator writing for a technical audience that is not specialized in the paper’s subfield, this summary will assume a medium-difficulty level. The paper explores the universality and reusability of neural representations, specifically focusing on latent spaces created by neural networks (NNs). The research aims to understand whether these representations can be reused or unified across different NN instances, adapting to factors such as randomness during training, model architecture, or data domain. The authors introduce the concept of Latent Communication, where similarities in latent representations emerge even from distinct or unrelated NNs. By exploiting a partial correspondence between data distributions, they found that representations can be projected into a universal representation (Relative Representation) or directly translated from one space to another. This phenomenon allows for a bridge between independently trained NNs, regardless of their training regimen, architecture, or data modality, as long as the semantic content remains the same. The universality of Latent Communication is demonstrated across various downstream tasks, including generation, classification, and retrieval, in supervised, weakly supervised, and unsupervised settings, and spans multiple data modalities such as images, text, audio, and graphs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how neural networks (NNs) create representations of input data and whether these representations can be reused or unified across different NN instances. The authors found that the representations created by a NN remain exclusive to a particular trained instance, but they also discovered a phenomenon called Latent Communication where similarities in latent representations emerge even from distinct or unrelated NNs. By exploiting a partial correspondence between data distributions, the authors showed that these representations can be projected into a universal representation or directly translated from one space to another. This allows for a bridge between independently trained NNs, regardless of their training regimen, architecture, or data modality. |
Keywords
» Artificial intelligence » Classification » Machine learning » Supervised » Unsupervised
