Can self-supervised learning handle both structured and unstructured data?

Yes, self-supervised learning (SSL) can handle both structured and unstructured data. SSL is a machine learning approach where models learn patterns by generating labels directly from the input data without relying on external annotations. This flexibility allows SSL to adapt to diverse data types by designing tasks that exploit inherent relationships within the data itself. For structured data (e.g., tabular datasets) and unstructured data (e.g., text, images), SSL frameworks create predictive tasks that force the model to learn meaningful representations, regardless of the data’s format.

For unstructured data, SSL has been widely applied in domains like natural language processing (NLP) and computer vision. In NLP, models like BERT use masked language modeling, where parts of a sentence are hidden, and the model predicts the missing words based on context. For images, methods like contrastive learning (e.g., SimCLR) train models to identify whether two augmented versions of an image (e.g., cropped or rotated) belong to the same original source. These tasks require the model to learn features like semantic relationships in text or visual patterns in images, all without labeled data.

SSL also works with structured data, though it’s less commonly discussed. For example, in tabular datasets, a model could predict missing values in one column using information from other columns (e.g., predicting a customer’s age based on purchase history). Time-series data, a type of structured data, can leverage SSL by training models to predict future values from past sequences (e.g., forecasting stock prices). Autoencoders—a type of neural network—can reconstruct input data after compression, learning latent representations of structured datasets. These approaches show SSL’s adaptability to structured formats by framing prediction tasks around the data’s existing relationships.

In summary, SSL’s strength lies in its ability to create supervision signals from the data’s structure or content, making it applicable across data types. Developers can implement SSL for unstructured data using established techniques like masked prediction or contrastive learning, while structured data can leverage tasks like missing value imputation or sequence prediction. The key is designing a pretext task that aligns with the data’s inherent patterns, enabling the model to learn useful representations without manual labeling.