One of the applications of center-based clustering algorithms such as K-Means is partitioning data points into K clusters.
In this work, we propose a learning framework to improve the shape bias property of self-supervised methods.
Ranked #34 on Domain Generalization on PACS
Our method can be applied to any layer of any arbitrary model without the need of any modification or additional training.
This paper presents a new feature selection method that efficiently carries out attribute reduction, thereby selecting the most informative features of a dataset.