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.

深度聚类存在过度自信问题，本研究开创性地提出了一个校准深度聚类框架，通过引入一个新的双头深度聚类流程来有效校准预测的置信度和实际准确性，通过规范化方法调整聚类流程中过度自信的预测，动态选择可靠的高置信度样本进行训练，并引入一种有效的网络初始化策略，提高训练速度和网络鲁棒性。广泛实验证明，该校准深度聚类框架在期望校准误差方面超过了最先进的深度聚类方法约 10 倍，并在聚类准确性方面显著优于它们。

朝着校准的深度聚类网络

Towards Calibrated Deep Clustering Network

Unsupervised image clustering methods often introduce alternative objectives
to indirectly train the model and are subject to faulty predictions and
overconfident results. To overcome these challenges, the current research
proposes an innovative model RUC that is inspired by robust learning. RUC's
novelty is at utilizing pseudo-labels of existing image clustering models as a
noisy dataset that may include misclassified samples. Its retraining process
can revise misaligned knowledge and alleviate the overconfidence problem in
predictions. The model's flexible structure makes it possible to be used as an
add-on module to other clustering methods and helps them achieve better
performance on multiple datasets. Extensive experiments show that the proposed
model can adjust the model confidence with better calibration and gain
additional robustness against adversarial noise.

本文提出了一种创新的 RUC 模型，利用已有的无监督图像聚类模型的伪标签作为噪声数据集，重新训练过程可以修正错误的知识并缓解预测中的过度自信问题。该模型具有良好的灵活性，可以用作其他聚类方法的附加模块，并帮助其在多个数据集上取得更好的性能。实验表明，该模型对于对手攻击的噪声具有额外的鲁棒性和更好的模型置信度校准。