methodology

Pseudo Labeling

Pseudo labeling is a semi-supervised learning technique where a model trained on labeled data is used to generate artificial labels (pseudo-labels) for unlabeled data, which are then added to the training set to improve model performance. It leverages large amounts of unlabeled data to enhance learning, often in scenarios where labeled data is scarce or expensive to obtain. This iterative process can help models generalize better and achieve higher accuracy, particularly in domains like computer vision and natural language processing.

Also known as: Pseudo-Labeling, Pseudo Label, Self-Training, Semi-Supervised Learning with Pseudo-Labels, PL
🧊Why learn Pseudo Labeling?

Developers should use pseudo labeling when working with limited labeled datasets, as it allows them to exploit abundant unlabeled data to boost model robustness and performance, such as in image classification or text analysis tasks. It is especially valuable in machine learning projects where data annotation is costly or time-consuming, enabling more efficient training cycles and potentially reducing overfitting by incorporating diverse examples. This technique is commonly applied in competitions and real-world applications to push the boundaries of model accuracy with constrained resources.

Compare Pseudo Labeling

Pseudo Labeling vs Active Learning→Pseudo Labeling vs Transfer Learning→Pseudo Labeling vs Few Shot Learning→

Learning Resources

📄
Pseudo-Labeling: The Simple and Efficient Semi-Supervised Learning Method
docs
→
📝
Semi-Supervised Learning with Pseudo-Labeling Tutorial
tutorial
→
🎬
Pseudo Labeling in Deep Learning - YouTube Video
video
→
📚
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow - Chapter on Semi-Supervised Learning
book
→

Related Tools

Semi Supervised LearningSelf TrainingData AugmentationActive LearningWeakly Supervised Learning

Alternatives to Pseudo Labeling

Active LearningTransfer LearningFew Shot Learning