Scaling Up Deep Clustering Methods Beyond ImageNet-1K
<|reference_start|>Scaling Up Deep Clustering Methods Beyond ImageNet-1K: Deep image clustering methods are typically evaluated on small-scale balanced classification datasets while feature-based $k$-means has been applied on proprietary billion-scale datasets. In this work, we explore the performance of feature-based deep clustering approaches on large-scale benchmarks whilst disentangling the impact of the following data-related factors: i) class imbalance, ii) class granularity, iii) easy-to-recognize classes, and iv) the ability to capture multiple classes. Consequently, we develop multiple new benchmarks based on ImageNet21K. Our experimental analysis reveals that feature-based $k$-means is often unfairly evaluated on balanced datasets. However, deep clustering methods outperform $k$-means across most large-scale benchmarks. Interestingly, $k$-means underperforms on easy-to-classify benchmarks by large margins. The performance gap, however, diminishes on the highest data regimes such as ImageNet21K. Finally, we find that non-primary cluster predictions capture meaningful classes (i.e. coarser classes).<|reference_end|>
@article{adaloglou2024scaling,
    title={Scaling Up Deep Clustering Methods Beyond ImageNet-1K},
    author={Nikolas Adaloglou and Felix Michels and Kaspar Senft and Diana
  Petrusheva and Markus Kollmann},
    journal={arXiv preprint arXiv:2406.01203},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.01203},
    primaryClass={cs.CV cs.AI cs.LG}
}