Dual Path

Dual-path architectures are a prominent theme in recent machine learning research, aiming to improve model performance by processing data through parallel pathways that capture complementary information. Current research focuses on applying this approach to diverse tasks, including speech enhancement, image processing, and time series forecasting, often incorporating transformers, convolutional recurrent networks, or other specialized modules within each path to optimize feature extraction and fusion. The resulting models demonstrate improved accuracy and efficiency compared to single-path alternatives across various applications, highlighting the effectiveness of this design principle for complex data analysis.