3 citations · 6 across the 9 of their papers we have counts for
4 papers · 1 filter
Breaking the Reclustering Barrier in Centroid-based Deep Clustering
Lukas Miklautz, Timo Klein, Kevin Sidak +5
This work investigates an important phenomenon in centroid-based deep clustering (DC) algorithms: Performance quickly saturates after a period of rapid early gains. Practitioners c…
Dying Clusters Is All You Need -- Deep Clustering With an Unknown Number of Clusters
Collin Leiber, Niklas Strauß, Matthias Schubert +1
Finding meaningful groups, i.e., clusters, in high-dimensional data such as images or texts without labeled data at hand is an important challenge in data mining. In recent years,…
SHADE: Deep Density-based Clustering
Anna Beer, Pascal Weber, Lukas Miklautz +4
Detecting arbitrarily shaped clusters in high-dimensional noisy data is challenging for current clustering methods. We introduce SHADE (Structure-preserving High-dimensional Analys…
Text-Guided Alternative Image Clustering
Andreas Stephan, Lukas Miklautz, Collin Leiber +4
Traditional image clustering techniques only find a single grouping within visual data. In particular, they do not provide a possibility to explicitly define multiple types of clus…