Yes, embeddings can be compressed to reduce storage requirements and improve computational efficiency. Compression techniques for embeddings generally aim to preserve the essential structure and relationships captured by the embeddings while reducing their size.
One common method is quantization, which reduces the precision of the numerical values in the embeddings. By using fewer bits to represent the values, the size of the embeddings is reduced, though there may be some trade-off in accuracy. Other techniques include sparse representations, where only the most important elements of the embeddings are retained, and knowledge distillation, where a smaller model is trained to approximate the output of a larger one, resulting in more compact embeddings.
Compressed embeddings can still be effective for many machine learning tasks, such as search and classification, as long as the compression process does not discard too much useful information.