Residual Transformer

Residual Transformers combine the strengths of Transformer networks, particularly their ability to capture long-range dependencies, with residual connections to improve training stability and performance. Current research focuses on adapting this architecture for various applications, including medical image analysis (e.g., generating ADC maps, brain tumor segmentation), time series forecasting, and speech processing, often incorporating techniques like low-rank weight sharing to reduce model size and computational cost. These advancements are significant because they enable the deployment of powerful Transformer models in resource-constrained environments and improve accuracy across a range of important tasks.