Visual Exploration of Neural Network Projection Stability
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Date
2022
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
We present a method to visually assess the stability of deep learned projections. For this, we perturb the high-dimensional data by controlled sequences and visualize the resulting changes in the 2D projection. We apply our method to a recent deep learned projection framework on several training configurations (learned projections and real-world datasets). Our method, which is simple to implement, runs at interactive rates, sheds several novel insights on the stability of the explored method.
Description
CCS Concepts: Human-centered computing --> Information visualization; Visual analytics; Visualization systems and tools
@inproceedings{10.2312:mlvis.20221068,
booktitle = {Machine Learning Methods in Visualisation for Big Data},
editor = {Archambault, Daniel and Nabney, Ian and Peltonen, Jaakko},
title = {{Visual Exploration of Neural Network Projection Stability}},
author = {Bredius, Carlo and Tian, Zonglin and Telea, Alexandru},
year = {2022},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-182-3},
DOI = {10.2312/mlvis.20221068}
}