Transformer Encoder

Transformer encoders are neural network architectures designed to process sequential data by leveraging self-attention mechanisms to capture long-range dependencies between input elements. Current research focuses on improving efficiency, particularly for large-scale applications, through techniques like sparsification, hierarchical representations, and dynamic depth adjustments, often within the context of specific model architectures such as Vision Transformers (ViTs) and variations of the Conformer. These advancements are driving progress in diverse fields, including image and video processing, speech recognition, medical image analysis, and autonomous driving, by enabling more robust and efficient solutions to complex tasks.