Abstract Constraint-based Deep Active Clustering (CODAC) integrates actively selected pairwise constraints into deep representation learning to efficiently improve existing cluster structures, even under tight query budgets. CODAC encodes the constraint information into the embedding so that the learned representation can generalize to unconstrained data, leading to a rapid improvement of the clustering quality even on large datasets. CODAC makes minimal assumptions regarding the data and can be combined with a wide variety of deep clustering models. It does not require the number of clusters to be known a priori and is even effective if the initial estimate is badly misspecified. Across diverse image, text, and tabular datasets, CODAC consistently attains higher cluster quality with fewer queries than the previous state-of-the-art, and can substantially improve the clustering quality with just 100–200 queries compared to the deep clustering baselines.

Active learning has the power to significantly reduce the amount of labeled data needed to build strong classifiers. Existing active pseudo-labeling methods show high potential in integrating pseudo-labels within the active learning loop but heavily depend on the prediction accuracy of the model. In this work, we propose VERIPS, an algorithm that significantly outperforms existing pseudo-labeling techniques for active learning. At its core, VERIPS uses a pseudo-label verification mechanism that consists of a second network only trained on data approved by the oracle and helps to discard questionable pseudo-labels. In particular, the verifier model eliminates all pseudo-labels for which it disagrees with the actual task model. VERIPS overcomes the problems of poorly performing initial models, e.g., due to imbalanced or too small initial pools, where previous methods select too many incorrect pseudo-labels and recovering takes long or is not possible. Moreover, VERIPS is particularly insensitive to parameter choices that existing approaches suffer from. Our code is available at https://github.com/lmu-dbs/VERIPS.