Dual Encoder Architecture

Dual encoder architectures are a prominent approach in machine learning that processes paired data (e.g., image-text, speech-text) by independently encoding each element before comparing their representations. Current research focuses on improving these architectures' efficiency and accuracy, exploring variations like asymmetric and Siamese designs, incorporating multi-modal data, and leveraging techniques such as knowledge distillation and masked autoencoders to enhance performance and reduce computational costs. This work has significant implications for various applications, including improved voice assistants, medical image analysis, video processing, and efficient text-image retrieval, particularly on resource-constrained devices.