Parametric deep clustering delivers strong image representations and partitions via modern contrastive and non-contrastive training, but it assumes a known number of clusters, K, which is often unrealistic in real datasets. Conversely, non-parametric methods estimate K but typically rely on weaker autoencoder features. We bridge this gap with AutoProPos, which extends the state-of-the-art ProPos and makes it non-parametric through a lightweight clustering supervisor (CLS). CLS alternates with ProPos and performs model selection over K in a reduced latent subspace using the average silhouette and the Silhouette Uniformity Index (SUI), with the latter encouraging uniform cluster distributions. Across image clustering benchmarks, AutoProPos is competitive with or superior to non-parametric deep clustering: 92.0% ACCon STL-10 (+11% vs. the best non-parametric baseline) and 77.0% ACC on ImageNet-50; against parametric deep clustering, it is also competitive and can even surpass them, as on ImageNet-Dogs, where it improves from 78.1% (ProPos) to 83.3% ACC. CLS estimates K during training with a small overhead (≤2 h on a single GPU), turning ProPos into a competitive non-parametric image-clustering method without sacrificing accuracy or compute.
Tepakbong et al. (Mon,) studied this question.