Dual Encoders

Dual encoders are a class of neural network architectures designed to efficiently learn separate representations for two distinct input modalities (e.g., text and images, or different audio channels), subsequently comparing these representations to assess similarity. Current research focuses on improving their efficiency and generalization capabilities across diverse tasks, including information retrieval, speech processing, and image segmentation, often employing techniques like contrastive learning and architectural modifications such as asymmetric or cascaded encoders. These advancements are significant because they enable faster and more scalable solutions for various applications, particularly in scenarios with large datasets or real-time constraints.