Decoder Layer

Decoder layers are a crucial component of many neural network architectures, particularly in sequence-to-sequence models and object detection, aiming to transform encoded information into a desired output format. Current research focuses on improving decoder efficiency and performance through architectural innovations, such as multi-scale decoders, decoupled architectures for handling class imbalances, and the incorporation of attention mechanisms to enhance feature alignment and reduce redundancy. These advancements are driving improvements in various applications, including medical image segmentation, object detection in remote sensing, and speech recognition, by enabling more accurate and efficient processing of complex data.