Hybrid Transformer

Hybrid transformers combine the strengths of convolutional neural networks (CNNs) and transformers, aiming to improve efficiency and accuracy in various applications. Current research focuses on developing novel hybrid architectures, such as those incorporating Swin-Unet, Mamba layers, and various attention mechanisms, to optimize performance for specific tasks like image processing, speech enhancement, and time series forecasting. These advancements are significant because they enable improved performance in computationally demanding tasks across diverse fields, from medical image analysis to resource-constrained edge devices.