Towards Calibrated Deep Clustering Network
Yuheng Jia Jianhong Cheng Hui Liu Junhui Hou
Deep clustering has exhibited remarkable performance; however, the
overconfidence problem, i.e., the estimated confidence for a sample belonging
to a particular cluster greatly exceeds its actual prediction accuracy, has
been overlooked in prior research. To tackle this critical issue, we pioneer
the development of a calibrated deep clustering framework. Specifically, we
propose a novel dual-head deep clustering pipeline that can effectively
calibrate the estimated confidence and the actual accuracy. The calibration
head adjusts the overconfident predictions of the clustering head using
regularization methods, generating prediction confidence and pseudo-labels that
match the model learning status. This calibration process also guides the
clustering head in dynamically selecting reliable high-confidence samples for
training. Additionally, we introduce an effective network initialization
strategy that enhances both training speed and network robustness. Extensive
experiments demonstrate the proposed calibrated deep clustering framework not
only surpasses state-of-the-art deep clustering methods by approximately 10
times in terms of expected calibration error but also significantly outperforms
them in terms of clustering accuracy.