Recursive Transformer

Recursive Transformers are a class of neural network architectures that leverage the power of transformers through repeated application of the same transformer block, aiming for improved efficiency and performance in various tasks. Current research focuses on developing recursive transformer models for applications like 3D human pose estimation, natural language understanding, and image processing, often incorporating techniques like model distillation and parameter sharing to create lightweight yet powerful models. These advancements are significant because they address the computational limitations of standard transformers, enabling their deployment in resource-constrained environments and improving the scalability of deep learning models for a wider range of applications.