Encoder Layer

Encoder layers are fundamental components of many deep learning models, tasked with extracting meaningful representations from input data (e.g., images, speech, text). Current research focuses on improving encoder layer efficiency, transferability, and interpretability, often employing transformer architectures and exploring techniques like self-supervised learning, knowledge distillation, and adaptive layer skipping to optimize performance and resource usage. These advancements are impacting various fields, from speech recognition and image processing to natural language processing and graph neural networks, by enabling more efficient and effective model training and deployment.