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.

翻译：不受监督的图像集集方法往往引入间接培训模型的替代目标,并受到错误预测和过度自信的结果的影响。为克服这些挑战,目前的研究提出了一种创新型的REC模型,这种模型的灵感来自强有力的学习。RUC的新颖之处是将现有图像集集模型的假标签用作可能包含分类错误样本的噪音数据集。它的再培训过程可以改变错误认识并缓解预测中的过度自信问题。模型的灵活结构使得有可能用作其他集成方法的附加模块,并帮助他们在多个数据集上取得更好的性能。广泛的实验表明,拟议的模型可以通过更好的校准来调整模型的信心,并获得更多的抵御对抗噪音的稳健性。