English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Deep Learning (DL) has shown great promise in the unsupervised task ofclustering. That said, while in classical (i.e., non-deep) clustering thebenefits of the nonparametric approach are well known, most deep-clusteringmethods are parametric: namely, they require a predefined and fixed number ofclusters, denoted by K. When K is unknown, however, using model-selectioncriteria to choose its optimal value might become computationally expensive,especially in DL as the training process would have to be repeated numeroustimes. In this work, we bridge this gap by introducing an effectivedeep-clustering method that does not require knowing the value of K as itinfers it during the learning. Using a split/merge framework, a dynamicarchitecture that adapts to the changing K, and a novel loss, our proposedmethod outperforms existing nonparametric methods (both classical and deepones). While the very few existing deep nonparametric methods lack scalability,we demonstrate ours by being the first to report the performance of such amethod on ImageNet. We also demonstrate the importance of inferring K byshowing how methods that fix it deteriorate in performance when their assumed Kvalue gets further from the ground-truth one, especially on imbalanceddatasets. Our code is available at https://github.com/BGU-CS-VIL/DeepDPM.

DeepDPM: Deep Clustering With an Unknown Number of Clusters