Dual Encoder Model

Dual encoder models are a class of efficient machine learning models that independently encode input data (e.g., images and text, questions and answers) into separate vector representations, enabling fast similarity comparisons for retrieval tasks. Current research focuses on improving their accuracy, particularly through techniques like knowledge distillation from more accurate but slower cross-encoder models, dynamic negative sampling strategies for more efficient training, and architectural modifications to enhance semantic understanding and handle paraphrases. These advancements are significant because they enable faster and more scalable solutions for various applications, including information retrieval, question answering, and 3D reconstruction, while addressing limitations in generalization and handling complex relationships between modalities.