Scaling Up Deep Clustering Methods Beyond ImageNet-1K
This work addresses the scalability of deep clustering methods for large-scale image datasets, providing insights for researchers in unsupervised learning, though it is incremental in benchmarking and analysis.
The paper investigates the performance of deep clustering methods on large-scale benchmarks, revealing that feature-based k-means is often unfairly evaluated on balanced datasets, while deep clustering outperforms k-means across most large-scale benchmarks, with performance gaps diminishing on the highest data regimes like ImageNet21K.
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).