How can you use unlabeled data to improve your AI models?

1 Semi-supervised learning

One of the most common ways to use unlabelled data is to combine it with a small amount of labeled data in a semi-supervised learning approach. This can help your AI model learn from both the features and the patterns of the data, and reduce the need for manual annotation. For example, you can use self-training, where your model first trains on the labeled data, then generates labels for the unlabeled data, and then retrains on the combined data. This can improve the accuracy and generalization of your model.

3 Self-supervised learning

A more advanced way to use unlabeled data is to leverage self-supervised learning, where your model creates its own labels or objectives from the data itself. This can help your AI model learn from the rich and diverse information in the data, and achieve state-of-the-art results on various tasks. For example, you can use contrastive learning, where your model learns to distinguish between different views or versions of the same data, or generative learning, where your model learns to generate new data or complete missing parts of the data.

4 Active learning

A more interactive way to use unlabeled data is to involve human feedback in an active learning process. This can help your AI model select the most informative or uncertain samples from the unlabeled data and ask for human labels or annotations. This can reduce the cost and time of data collection and labeling and increase the quality and diversity of the data. An example of this is the use of query strategies, where your model ranks the unlabeled data based on some criteria, such as entropy, margin, or diversity, and requests the human labels for the top-ranked samples.

5 Transfer learning

A more flexible way to use unlabeled data is to adapt your AI model from a pre-trained model that was trained on a large-scale unlabeled data set. This can help your AI model leverage the knowledge and skills learned from the pre-trained model and fine-tune them to a specific task or domain. This can improve the efficiency and effectiveness of your model and overcome the limitations of small or noisy labeled data sets. For example, you can use BERT, a pre-trained language model that was trained on a huge corpus of unlabelled text, and fine-tune it for various natural language-processing tasks, such as sentiment analysis, question answering, or text summarization.

6 Data augmentation

A more creative way to use unlabelled data is to generate more synthetic or modified data from the existing data and use it to augment your AI model. This can help your AI model increase the size and diversity of the data and prevent overfitting or bias. One way to do this is to use image augmentation, where you apply transformations, such as cropping, flipping, rotating, or adding noise, to the images, or text augmentation, where you use synonyms, paraphrasing, or back-translation to the text.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?