(The Eurographics Association, 2022) Bredius, Carlo; Tian, Zonglin; Telea, Alexandru; Archambault, Daniel; Nabney, Ian; Peltonen, Jaakko
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.