Sparse Transformer

Sparse transformers aim to improve the efficiency and scalability of standard transformers by reducing computational complexity, primarily by selectively attending to only a subset of input tokens. Current research focuses on developing novel sparse attention mechanisms, including various windowing strategies, hierarchical structures, and adaptive pruning techniques, often integrated into architectures like Swin Transformers and Universal Transformers. This research is significant because it enables the application of transformer models to larger datasets and more complex tasks, particularly in resource-constrained environments, with applications spanning image processing, natural language processing, and autonomous driving.