This work investigates an important phenomenon in centroid-based deep clustering (DC) algorithms: Performance quickly saturates after a period of rapid early gains. Practitioners commonly address early saturation with periodic reclustering, which we demonstrate to be insufficient to address performance plateaus. We call this phenomenon the “reclustering barrier” and empirically show when the reclustering barrier occurs, what its underlying mechanisms are, and how it is possible to Break the Reclustering Barrier with our algorithm BRB. BRB avoids early over-commitment to initial clusterings and enables continuous adaptation to reinitialized clustering targets while remaining conceptually simple. Applying our algorithm to widely-used centroid-based DC algorithms, we show that (1) BRB consistently improves performance across a wide range of clustering benchmarks, (2) BRB enables training from scratch, and (3) BRB performs competitively against state-of-the-art DC algorithms when combined with a contrastive loss. We release our code and pre-trained models at https://github.com/Probabilistic-and-Interactive-ML/breaking-the-reclustering-barrier .

We introduce H-SPLID, a novel algorithm for learning salient feature representations through the explicit decomposition of salient and non-salient features into separate spaces. We show that H-SPLID promotes learning low-dimensional, task-relevant features. We prove that the expected prediction deviation under input perturbations is upper-bounded by the dimension of the salient subspace and the Hilbert-Schmidt Independence Criterion (HSIC) between inputs and representations. This establishes a link between robustness and latent representation compression in terms of the dimensionality and information preserved. Empirical evaluations on image classification tasks show that models trained with H-SPLID primarily rely on salient input components, as indicated by reduced sensitivity to perturbations affecting non-salient features, such as image backgrounds.

Glass beads were among the most common grave goods in the Early Middle Ages, with an estimated number in the millions. The color, size, shape and decoration of the beads are diverse leading to many different archaeological classification systems that depend on the subjective decisions of individual experts. The lack of an agreed upon expert categorization leads to a pressing problem in archaeology, as the categorization of archaeological artifacts, like glass beads, is important to learn about cultural trends, manufacturing processes or economic relationships (e.g., trade routes) of historical times. An automated, objective and reproducible classification system is therefore highly desirable. We present a high-quality data set of images of Early Medieval beads and propose a clustering pipeline to learn a classification system in a data-driven way. The pipeline consists of a novel extension of deep embedded non-redundant clustering to identify multiple, meaningful clusterings of glass bead images. During the cluster analysis we address several challenges associated with the data and as a result identify high-quality clusterings that overlap with archaeological domain expertise. To the best of our knowledge this is the first application of non-redundant image clustering for archaeological data.