# Density-Aware Graph for Deep Semi-Supervised Visual Recognition

arXiv: https://arxiv.org/abs/2003.13194 · HTML: https://mlsanity.com/paper/2003.13194/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Mar 2020.

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

- [A Survey on Deep Semi-supervised Learning](https://mlsanity.com/paper/2103.00550/) (2021)
- [Mining Latent Classes for Few-shot Segmentation](https://mlsanity.com/paper/2103.15402/) (2021)
- [Semi-Supervised and Unsupervised Deep Visual Learning: A Survey](https://mlsanity.com/paper/2208.11296/) (2022)
- [Incremental Generalized Category Discovery](https://mlsanity.com/paper/2304.14310/) (2023)
- [Barely-Supervised Learning: Semi-Supervised Learning with very few labeled images](https://mlsanity.com/paper/2112.12004/) (2021)
- [Improve Unsupervised Pretraining for Few-label Transfer](https://mlsanity.com/paper/2107.12369/) (2021)
- [MaxMatch: Semi-Supervised Learning with Worst-Case Consistency](https://mlsanity.com/paper/2209.12611/) (2022)
- [An Empirical Study and Analysis on Open-Set Semi-Supervised Learning](https://mlsanity.com/paper/2101.08237/) (2021)
- [LaplaceNet: A Hybrid Graph-Energy Neural Network for Deep Semi-Supervised Classification](https://mlsanity.com/paper/2106.04527/) (2021)
- [AggMatch: Aggregating Pseudo Labels for Semi-Supervised Learning](https://mlsanity.com/paper/2201.10444/) (2022)
- [Towards Self-Adaptive Pseudo-Label Filtering for Semi-Supervised Learning](https://mlsanity.com/paper/2309.09774/) (2023)
- [Density-Aware Hyper-Graph Neural Networks for Graph-based Semi-supervised Node Classification](https://mlsanity.com/paper/2201.11511/) (2022)
- [DualHGNN: A Dual Hypergraph Neural Network for Semi-Supervised Node Classification based on Multi-View Learning and Density Awareness](https://mlsanity.com/paper/2306.04214/) (2023)
- [Probability-density-aware Semi-supervised Learning](https://mlsanity.com/paper/2412.17547/) (2024)
- [MTSSL: Meta-Thresholding Semi-Supervised Learning](https://mlsanity.com/paper/2607.16363/) (2026)

## Abstract (arXiv)

Semi-supervised learning (SSL) has been extensively studied to improve the generalization ability of deep neural networks for visual recognition. To involve the unlabelled data, most existing SSL methods are based on common density-based cluster assumption: samples lying in the same high-density region are likely to belong to the same class, including the methods performing consistency regularization or generating pseudo-labels for the unlabelled images. Despite their impressive performance, we argue three limitations exist: 1) Though the density information is demonstrated to be an important clue, they all use it in an implicit way and have not exploited it in depth. 2) For feature learning, they often learn the feature embedding based on the single data sample and ignore the neighborhood information. 3) For label-propagation based pseudo-label generation, it is often done offline and difficult to be end-to-end trained with feature learning. Motivated by these limitations, this paper proposes to solve the SSL problem by building a novel density-aware graph, based on which the neighborhood information can be easily leveraged and the feature learning and label propagation can also be trained in an end-to-end way. Specifically, we first propose a new Density-aware Neighborhood Aggregation(DNA) module to learn more discriminative features by incorporating the neighborhood information in a density-aware manner. Then a novel Density-ascending Path based Label Propagation(DPLP) module is proposed to generate the pseudo-labels for unlabeled samples more efficiently according to the feature distribution characterized by density. Finally, the DNA module and DPLP module evolve and improve each other end-to-end.