  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.
