# Circles: Inter-Model Comparison of Multi-Classification Problems with High Number of Classes

arXiv: https://arxiv.org/abs/2309.05672 · HTML: https://mlsanity.com/paper/2309.05672/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 15 references that are in the map.
- First posted on arXiv 8 Sep 2023.

## Builds on (most cited first, 4 of 4)

- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Do Convolutional Neural Networks Learn Class Hierarchy?](https://mlsanity.com/paper/1710.06501/) (2017)
- [Neo: Generalizing Confusion Matrix Visualization to Hierarchical and Multi-Output Labels](https://mlsanity.com/paper/2110.12536/) (2021)
- [A User-based Visual Analytics Workflow for Exploratory Model Analysis](https://mlsanity.com/paper/1809.10782/) (2018)

## Abstract (arXiv)

The recent advancements in machine learning have motivated researchers to generate classification models dealing with hundreds of classes such as in the case of image datasets. However, visualization of classification models with high number of classes and inter-model comparison in such classification problems are two areas that have not received much attention in the literature, despite the ever-increasing use of classification models to address problems with very large class categories. In this paper, we present our interactive visual analytics tool, called Circles, that allows a visual inter-model comparison of numerous classification models with 1K classes in one view. To mitigate the tricky issue of visual clutter, we chose concentric a radial line layout for our inter-model comparison task. Our prototype shows the results of 9 models with 1K classes