Hybrid embeddings refer to representations that combine multiple types of embeddings or modalities to capture richer, more comprehensive information. In scenarios where data comes from multiple sources or formats, hybrid embeddings combine the features of each modality into a unified representation. For example, a hybrid embedding might combine text embeddings (e.g., BERT embeddings for natural language) with image embeddings (e.g., CNN features) to represent both textual and visual data together.
Hybrid embeddings are commonly used in multimodal applications, where integrating information from different sources leads to a better understanding of the data. A classic example is in cross-modal retrieval systems, where the model needs to compare images with text. By combining embeddings from both modalities, the system can match images with their descriptive text or vice versa, even if the query is only in one modality.
These embeddings are typically learned through methods like joint learning or multi-task learning, where the model is trained to encode information from multiple sources into a shared embedding space. Hybrid embeddings improve model performance in complex tasks by capturing diverse information and enabling models to make more accurate predictions or generate more meaningful outputs in applications like recommendation systems, cross-modal search, and multimedia understanding.