AutoProPos: An Extension of Prototype Scattering and Positive Sampling Clustering for an Unknown Number of Clusters
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".