In self-supervised learning (SSL), embeddings are compact numerical representations of data that capture essential features or patterns. Their primary role is to enable models to learn meaningful relationships in unlabeled data by converting raw inputs (like text, images, or audio) into vectors in a lower-dimensional space. These embeddings act as a bridge between the raw data and the model’s ability to generalize, allowing downstream tasks (e.g., classification or clustering) to leverage pre-trained knowledge efficiently. For example, in natural language processing (NLP), embeddings might represent words or sentences in a way that preserves semantic similarity, making it easier for models to detect relationships like synonyms or contextual meaning.
A key use case for embeddings in SSL is in contrastive learning frameworks. Here, the model learns by comparing pairs of data points. For instance, in computer vision, an SSL model might generate embeddings for two augmented versions of the same image (e.g., cropped or rotated) and train to make their embeddings similar, while pushing embeddings of different images apart. This forces the model to encode high-level features (like object shapes) rather than superficial details. Similarly, in NLP, models like BERT create embeddings by predicting masked words in sentences. The resulting embeddings capture contextual relationships, allowing the model to understand that “bank” in “river bank” differs from “bank account” based on surrounding words. These embeddings become reusable features for tasks like sentiment analysis or named entity recognition.
Embeddings also reduce reliance on labeled data. Since SSL trains on unlabeled datasets, the embeddings encapsulate general-purpose knowledge that can be fine-tuned with minimal labeled examples. For example, a vision model pre-trained on millions of unlabeled images via SSL can produce embeddings that help a medical imaging classifier achieve high accuracy with only a few hundred labeled X-rays. Additionally, embeddings enable efficient computation. Instead of processing raw pixels or text tokens repeatedly, downstream tasks operate on the smaller, semantically rich embedding vectors. This balance between compression and retained information makes embeddings a foundational component in SSL workflows, directly impacting model performance, scalability, and adaptability across domains.