  We propose Few-Example Clustering (FEC), a novel algorithm that performs
contrastive learning to cluster few examples. Our method is composed of the
following three steps: (1) generation of candidate cluster assignments, (2)
contrastive learning for each cluster assignment, and (3) selection of the best
candidate. Based on the hypothesis that the contrastive learner with the
ground-truth cluster assignment is trained faster than the others, we choose
the candidate with the smallest training loss in the early stage of learning in
step (3). Extensive experiments on the \textit{mini}-ImageNet and CUB-200-2011
datasets show that FEC outperforms other baselines by about 3.2% on average
under various scenarios. FEC also exhibits an interesting learning curve where
clustering performance gradually increases and then sharply drops.
