Summary of From Logits to Hierarchies: Hierarchical Clustering Made Simple, by Emanuele Palumbo et al.
From Logits to Hierarchies: Hierarchical Clustering made Simple
by Emanuele Palumbo, Moritz Vandenhirtz, Alain Ryser, Imant Daunhawer, Julia E. Vogt
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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 The proposed approach challenges recent advancements in hierarchical clustering with deep architectures by demonstrating limitations when applied to realistic datasets. By building upon pre-trained non-hierarchical clustering models and implementing a lightweight procedure, the authors outperform models specifically designed for hierarchical clustering. This efficient method can be applied to any pre-trained clustering model outputting logits without requiring fine-tuning. The approach is further demonstrated in a supervised setup, recovering meaningful hierarchies from a pre-trained ImageNet classifier. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how we can group similar things together in a smart way. Usually, this involves using special models that are good at finding patterns within groups of things. However, these models can be slow and don’t always work well with real-world data. The researchers found a better way to do hierarchical clustering by building on simpler models that are already good at grouping things. This new approach is fast and can even be used in situations where we want to group images based on what they look like. |
Keywords
» Artificial intelligence » Clustering » Fine tuning » Hierarchical clustering » Logits » Supervised
