Transformer Encoder Layer

Transformer encoder layers are fundamental building blocks in many deep learning models, aiming to efficiently process sequential data by capturing long-range dependencies. Current research focuses on improving their efficiency and adaptability, exploring techniques like parameter-efficient fine-tuning with adapters, learning specialized tokens for domain adaptation, and employing low-rank approximations and weight sharing to reduce model size. These advancements are crucial for deploying sophisticated models on resource-constrained devices and improving performance across diverse applications, including speech processing, image classification, and object tracking.