Few-Example Clustering via Contrastive Learning

We propose Few-Example Clustering (FEC), a In this paper, we propose Few-Example Clustering (FEC), a novel algorithm that performs contrastive learning novel clustering algorithm based on the hypothesis that the to cluster few examples. Our method is composed contrastive learner with the ground-truth cluster assignment of the following three steps: (1) generation of candidate is trained faster than the others. This hypothesis is built on cluster assignments, (2) contrastive learning the phenomenon that deep neural networks initially learn for each cluster assignment, and (3) selection patterns from the training examples. FEC is composed of of the best candidate. Based on the hypothesis the following three steps (see Figure 1): (1) generation of that the contrastive learner with the ground-truth candidate cluster assignments, (2) contrastive learning for cluster assignment is trained faster than the others, each cluster assignment, and (3) selection of the best candidate.