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.
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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of Machine Learning in Visualization (MLVis) |
| Number of pages | 5 |
| DOIs | |
| Publication status | Published - 2022 |