# Label Propagation for Deep Semi-supervised Learning

## TL;DR

This paper introduces a transductive label propagation method based on the manifold assumption to enhance semi-supervised deep learning, especially effective with limited labeled data, by iteratively generating pseudo-labels and training neural networks.

## Contribution

It adapts classic transductive label propagation to deep learning, leveraging neural embeddings for improved semi-supervised learning in an inductive framework.

## Key findings

- Improved performance on multiple datasets with few labels
- Complementary to existing state-of-the-art methods
- Effective in low-label regimes

## Abstract

Semi-supervised learning is becoming increasingly important because it can combine data carefully labeled by humans with abundant unlabeled data to train deep neural networks. Classic methods on semi-supervised learning that have focused on transductive learning have not been fully exploited in the inductive framework followed by modern deep learning. The same holds for the manifold assumption---that similar examples should get the same prediction. In this work, we employ a transductive label propagation method that is based on the manifold assumption to make predictions on the entire dataset and use these predictions to generate pseudo-labels for the unlabeled data and train a deep neural network. At the core of the transductive method lies a nearest neighbor graph of the dataset that we create based on the embeddings of the same network.Therefore our learning process iterates between these two steps. We improve performance on several datasets especially in the few labels regime and show that our work is complementary to current state of the art.