# Enhancing Classification with Semi-Supervised Deep Learning Using Distance-Based Sample Weights

arXiv: https://arxiv.org/abs/2505.14345 · HTML: https://mlsanity.com/paper/2505.14345/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 38 references that are in the map.
- First posted on arXiv 20 May 2025.

## Builds on (most cited first, 13 of 13)

- [Semi-Supervised Learning with Deep Generative Models](https://mlsanity.com/paper/1406.5298/) (2014)
- [Recent Trends in Deep Learning Based Natural Language Processing](https://mlsanity.com/paper/1708.02709/) (2017)
- [A Survey on Deep Semi-supervised Learning](https://mlsanity.com/paper/2103.00550/) (2021)
- [Model Complexity of Deep Learning: A Survey](https://mlsanity.com/paper/2103.05127/) (2021)
- [CNN Image Retrieval Learns from BoW: Unsupervised Fine-Tuning with Hard Examples](https://mlsanity.com/paper/1604.02426/) (2016)
- [Deep Co-Training for Semi-Supervised Image Segmentation](https://mlsanity.com/paper/1903.11233/) (2019)
- [A survey on Semi-, Self- and Unsupervised Learning for Image Classification](https://mlsanity.com/paper/2002.08721/) (2020)
- [DASO: Distribution-Aware Semantics-Oriented Pseudo-label for Imbalanced Semi-Supervised Learning](https://mlsanity.com/paper/2106.05682/) (2021)
- [Natural Language Processing Advancements By Deep Learning: A Survey](https://mlsanity.com/paper/2003.01200/) (2020)
- [When Face Recognition Meets with Deep Learning: an Evaluation of Convolutional Neural Networks for Face Recognition](https://mlsanity.com/paper/1504.02351/) (2015)
- [A Survey on Semi-Supervised Learning for Delayed Partially Labelled Data Streams](https://mlsanity.com/paper/2106.09170/) (2021)
- [Correcting Data Imbalance for Semi-Supervised Covid-19 Detection Using X-ray Chest Images](https://mlsanity.com/paper/2008.08496/) (2020)
- [Recent Advances in Deep Learning Techniques for Face Recognition](https://mlsanity.com/paper/2103.10492/) (2021)

## Abstract (arXiv)

Recent advancements in semi-supervised deep learning have introduced effective strategies for leveraging both labeled and unlabeled data to improve classification performance. This work proposes a semi-supervised framework that utilizes a distance-based weighting mechanism to prioritize critical training samples based on their proximity to test data. By focusing on the most informative examples, the method enhances model generalization and robustness, particularly in challenging scenarios with noisy or imbalanced datasets. Building on techniques such as uncertainty consistency and graph-based representations, the approach addresses key challenges of limited labeled data while maintaining scalability. Experiments on twelve benchmark datasets demonstrate significant improvements across key metrics, including accuracy, precision, and recall, consistently outperforming existing methods. This framework provides a robust and practical solution for semi-supervised learning, with potential applications in domains such as healthcare and security where data limitations pose significant challenges.