Summary of Measuring Similarity Between Embedding Spaces Using Induced Neighborhood Graphs, by Tiago F. Tavares et al.
Measuring similarity between embedding spaces using induced neighborhood graphs
by Tiago F. Tavares, Fabio Ayres, Paris Smaragdis
First submitted to arxiv on: 13 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 metric evaluates the similarity between paired item representations by analyzing the structural similarity between the nearest-neighbors induced graphs of each representation. This allows for comparing spaces based on different distance metrics and neighborhood sizes. The method is demonstrated through two case studies: an analogy task using GloVe embeddings and zero-shot classification in the CIFAR-100 dataset using CLIP embeddings. Results show that accuracy in both tasks correlates with the embedding similarity, which may help explain performance differences and lead to improved paired-embedding model design. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about how deep learning can be used to understand relationships between things. It’s all about creating special maps called “embedding spaces” that show how similar or different things are. These maps are really useful because they let us do cool tasks like finding new similarities and classifying images without needing more training data. The authors of this paper want to know if these maps are good at capturing the right relationships, so they came up with a way to measure their similarity. They tested it on two examples: matching words that mean the same thing and classifying pictures without looking at them before. What they found is that how similar the maps are affects how well the tasks work out. |
Keywords
* Artificial intelligence * Classification * Deep learning * Embedding * Glove * Zero shot
