# Rare Galaxy Classes Identified In Foundation Model Representations

arXiv: https://arxiv.org/abs/2312.02910 · HTML: https://mlsanity.com/paper/2312.02910/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 24 references that are in the map.
- First posted on arXiv 5 Dec 2023.

## Builds on (most cited first, 5 of 5)

- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [MaxViT: Multi-Axis Vision Transformer](https://mlsanity.com/paper/2204.01697/) (2022)
- [Practical Galaxy Morphology Tools from Deep Supervised Representation Learning](https://mlsanity.com/paper/2110.12735/) (2021)
- [Towards Galaxy Foundation Models with Hybrid Contrastive Learning](https://mlsanity.com/paper/2206.11927/) (2022)
- [Mining for Strong Gravitational Lenses with Self-supervised Learning](https://mlsanity.com/paper/2110.00023/) (2021)

## Built on by (most cited first, 1 of 1)

- [PAPERCLIP: Associating Astronomical Observations and Natural Language with Multi-Modal Models](https://mlsanity.com/paper/2403.08851/) (2024)

## Abstract (arXiv)

We identify rare and visually distinctive galaxy populations by searching for structure within the learned representations of pretrained models. We show that these representations arrange galaxies by appearance in patterns beyond those needed to predict the pretraining labels. We design a clustering approach to isolate specific local patterns, revealing groups of galaxies with rare and scientifically-interesting morphologies.