Embeddings play a key role in Self-Supervised Learning (SSL) by providing a way to represent data in a more manageable and meaningful way. In SSL, the main idea is to learn useful features from the data without the need for labeled examples. Embeddings convert raw input data, such as images, text, or audio, into vectors in a lower-dimensional space that capture the underlying patterns and relationships in the data. This transformation allows models to focus on the essential characteristics that are significant for various tasks, such as classification or similarity search.
For instance, in natural language processing tasks, words can be turned into embeddings using techniques like Word2Vec or GloVe. These embeddings map words into a continuous vector space where words with similar meanings are closer together. As a result, a model trained on a large corpus of text can learn to understand context and semantics without explicit labeling. Similarly, for images, convolutional neural networks (CNNs) can generate embeddings that represent visual features, such as edges or textures, enabling the model to recognize objects or classify images effectively without annotated data.
In practice, self-supervised methods often utilize these embeddings during training to maximize a consistency loss or similarity measure. For example, a common approach is to create different views of the same data point, like augmenting an image with rotations or crops. The embeddings of these different views are then trained to be similar, which helps the model learn robust features. As a result, embeddings serve as a bridge between raw data and useful representations, facilitating more efficient learning and improving the performance of models in various tasks.