Clustering is important task in machine learning, deep learning and industry. Especially in deep learning, clustering is becoming increasingly important because it is used not only for clustering but also for other purposes such as pretext task in self-supervised learning. Many previous literatures have shown excellent performance in benchmark datasets, but these datasets are not rotated. However, in practically there is no guarantee that the dataset will always be placed right. We tackle that existing prior clustering algorithms do not work well when images are randomly rotated.
In this talk, by leveraging Implicit Neural Representations(INR),
1. We obtain a latent vector, where rotation angle of the data and rotationally invariant latent structure vectors are disentangled from each other.
2. We show that clustering by rotationally invariant latent structure vectors have superior performance than other methods.
To best of our knowledge, it is the first approach to cluster with Implicit Neural Representations.