Improving Unsupervised Image Clustering With Robust Learning

Understand

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.