# Semi-supervised deep learning based on label propagation in a 2D embedded space

arXiv: https://arxiv.org/abs/2008.00558 · HTML: https://mlsanity.com/paper/2008.00558/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2020.

## Built on by (most cited first, 4 of 4)

- [Iterative Pseudo-Labeling with Deep Feature Annotation and Confidence-Based Sampling](https://mlsanity.com/paper/2109.02717/) (2021)
- [Few-Shot Image Classification Along Sparse Graphs](https://mlsanity.com/paper/2112.03951/) (2021)
- [Linking data separation, visual separation, and classifier performance using pseudo-labeling by contrastive learning](https://mlsanity.com/paper/2302.02663/) (2023)
- [Standardness Clouds Meaning: A Position Regarding the Informed Usage of Standard Datasets](https://mlsanity.com/paper/2406.13552/) (2024)

## Abstract (arXiv)

While convolutional neural networks need large labeled sets for training images, expert human supervision of such datasets can be very laborious. Proposed solutions propagate labels from a small set of supervised images to a large set of unsupervised ones to obtain sufficient truly-and-artificially labeled samples to train a deep neural network model. Yet, such solutions need many supervised images for validation. We present a loop in which a deep neural network (VGG-16) is trained from a set with more correctly labeled samples along iterations, created by using t-SNE to project the features of its last max-pooling layer into a 2D embedded space in which labels are propagated using the Optimum-Path Forest semi-supervised classifier. As the labeled set improves along iterations, it improves the features of the neural network. We show that this can significantly improve classification results on test data (using only 1% to 5% of supervised samples) of three private challenging datasets and two public ones.