# MultiCaM-Vis: Visual Exploration of Multi-Classification Model with High Number of Classes

arXiv: https://arxiv.org/abs/2309.05676 · HTML: https://mlsanity.com/paper/2309.05676/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 9 references that are in the map.
- First posted on arXiv 9 Sep 2023.

## Builds on (most cited first, 2 of 2)

- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Towards Better Analysis of Deep Convolutional Neural Networks](https://mlsanity.com/paper/1604.07043/) (2016)

## Abstract (arXiv)

Visual exploration of multi-classification models with large number of classes would help machine learning experts in identifying the root cause of a problem that occurs during learning phase such as miss-classification of instances. Most of the previous visual analytics solutions targeted only a few classes. In this paper, we present our interactive visual analytics tool, called MultiCaM-Vis, that provides Emphoverview+detail style parallel coordinate views and a Chord diagram for exploration and inspection of class-level miss-classification of instances. We also present results of a preliminary user study with 12 participants.