A Primer on Semi-Supervised Learning — Part 1

Semi-Supervised Learning (SSL) is a Machine Learning technique where a task is learned from a small labeled dataset and relatively larger unlabeled data. The objective of SSL is to perform better than a supervised learning technique trained using labeled data alone. This is Part 1 of the article series on Semi-Supervised Learning and gives a brief introduction to this important sub-domain of Machine Learning. Future parts cover SSL approaches in detail.

Photo by Franck V. on Unsplash

Outline Part 1:

1. Distinguishing Semi-supervised Learning from Supervised and Unsupervised Learning?
2. Why should we care about Semi-Supervised Learning?
3. Examples of Semi-Supervised Learning Tasks
4. Conclusions and future Parts

Outline Future Parts:

1. Consistency Regularization, Entropy Minimization, and Pseudo Labeling
2. Approaches for Semi-Supervised Learning
   — Π model
   — Temporal Ensembling
   — Mean Teacher method
   — Unsupervised Data Augmentation
   — MixMatch

Part 2 is available here.

Distinguishing Semi-supervised Learning from Supervised and Unsupervised Learning?

The extent of labeled data in the entire dataset…