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Few-Example Clustering via Contrastive Learning

arXiv 2207.04050 ↗First posted 8 Jul 2022cs.LG cs.AIcited by 0

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Abstract (arXiv)

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 textitmini-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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 31 of its 48 references that are in the map. First posted on arXiv 8 Jul 2022.

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31 of 48 references.

References · 31

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19 more
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