Transformer Variant

Transformer variants are modified versions of the original Transformer architecture, aiming to improve efficiency, performance, or applicability across diverse data modalities (text, vision, speech). Current research focuses on enhancing attention mechanisms (e.g., sparse attention, linear attention), optimizing model architectures for specific tasks (e.g., long-range dependencies, multi-step forecasting), and integrating explainable AI techniques for better interpretability. These advancements are significant because they enable the application of Transformers to increasingly complex problems and larger datasets, impacting fields like natural language processing, computer vision, and hydrological modeling.