# Semi-Supervised Learning via Compact Latent Space Clustering

arXiv: https://arxiv.org/abs/1806.02679 · HTML: https://mlsanity.com/paper/1806.02679/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2018.

## Built on by (most cited first, 15 of 15)

- [Instance-Dependent Partial Label Learning](https://mlsanity.com/paper/2110.12911/) (2021)
- [On the Importance of Distractors for Few-Shot Classification](https://mlsanity.com/paper/2109.09883/) (2021)
- [Clustering Aided Weakly Supervised Training to Detect Anomalous Events in Surveillance Videos](https://mlsanity.com/paper/2203.13704/) (2022)
- [Context-self contrastive pretraining for crop type semantic segmentation](https://mlsanity.com/paper/2104.04310/) (2021)
- [Ego-Vehicle Action Recognition based on Semi-Supervised Contrastive Learning](https://mlsanity.com/paper/2303.00977/) (2023)
- [Intraclass clustering: an implicit learning ability that regularizes DNNs](https://mlsanity.com/paper/2103.06733/) (2021)
- [Inversion dynamics of class manifolds in deep learning reveals tradeoffs underlying generalisation](https://mlsanity.com/paper/2303.05161/) (2023)
- [BERTops: Studying BERT Representations under a Topological Lens](https://mlsanity.com/paper/2205.00953/) (2022)
- [Fermi-Bose Machine achieves both generalization and adversarial robustness](https://mlsanity.com/paper/2404.13631/) (2024)
- [A Generalized Weighted Optimization Method for Computational Learning and Inversion](https://mlsanity.com/paper/2201.09223/) (2022)
- [Towards understanding deep learning with the natural clustering prior](https://mlsanity.com/paper/2203.08174/) (2022)
- [Decompositional Generation Process for Instance-Dependent Partial Label Learning](https://mlsanity.com/paper/2204.03845/) (2022)
- [Contrastive Credibility Propagation for Reliable Semi-Supervised Learning](https://mlsanity.com/paper/2211.09929/) (2022)
- [Imbalanced Semi-Supervised Learning via Label Refinement and Threshold Adjustment](https://mlsanity.com/paper/2407.05370/) (2024)
- [Unsupervised Anomaly Detection in NSL-KDD Using β-VAE: A Latent Space and Reconstruction Error Approach](https://mlsanity.com/paper/2602.19785/) (2026)

## Abstract (arXiv)

We present a novel cost function for semi-supervised learning of neural networks that encourages compact clustering of the latent space to facilitate separation. The key idea is to dynamically create a graph over embeddings of labeled and unlabeled samples of a training batch to capture underlying structure in feature space, and use label propagation to estimate its high and low density regions. We then devise a cost function based on Markov chains on the graph that regularizes the latent space to form a single compact cluster per class, while avoiding to disturb existing clusters during optimization. We evaluate our approach on three benchmarks and compare to state-of-the art with promising results. Our approach combines the benefits of graph-based regularization with efficient, inductive inference, does not require modifications to a network architecture, and can thus be easily applied to existing networks to enable an effective use of unlabeled data.