ReSL: Enhancing Deep Clustering Through Reset-based Self-Labeling

The goal of clustering is to group similar data points together. Deep clustering enhances this process by using neural networks for inferring better data representations through a three-stage approach: pre-training for initial feature learning, deep clustering to structure the latent space, and self-labeling to iteratively refine both representations and cluster assignments. Ever since its inception, self-labeling has been a crucial element for reaching state-of-the-art performance in deep clustering. The samples for the self-labeling phase are obtained by setting a confidence threshold for the network’s predictions and only using samples that exceed this threshold for further training. This often improves clustering performance but relies on training with noisy, self-constructed labels (pseudo-labels). As the model iteratively retrains on its own pseudo-labels, the certainty of its predictions tends to rise, increasing its confidence over time. The increasing confidence leads to a growing number of training samples also including more and more samples assigned to the wrong cluster, which can limit performance. Particularly, the model's initially learned biases are amplified by relying on easily learned but ultimately misleading patterns in pseudo-labels, hampering generalization.In this paper, we propose ReSL, a framework that unites Resets with Self-Labeling. We demonstrate that employing weight-reset techniques during self-labeling increases clustering performance and improves generalization. Our findings address limitations of self-labeling and provide a foundation for future research in developing more robust approaches.