Dynamic Transformer

Dynamic transformers are a class of neural network architectures designed to adapt their computational resources to the specific demands of individual inputs, improving efficiency and performance on various tasks. Current research focuses on developing dynamic routing mechanisms within transformer models, often employing techniques like multi-scale blocks, contextual bandits, and multi-exit strategies to optimize resource allocation. These advancements are impacting diverse fields, including image processing (super-resolution, deblurring, inpainting), video processing, object tracking, and even brain network analysis, by enabling more efficient and accurate models for complex data. The resulting models offer improved speed-accuracy trade-offs compared to static architectures.