Deep Complex Network

Deep complex networks (DCNs) extend traditional deep learning by leveraging complex numbers, enabling the incorporation of phase information alongside magnitude, particularly beneficial for processing signals with inherent phase dependencies like audio and images. Current research focuses on developing DCN architectures for tasks such as anomaly detection, speech processing (dereverberation and echo cancellation), and image classification, often incorporating techniques like self-attention and multi-frame filtering to improve performance. These advancements offer improved accuracy and robustness compared to real-valued counterparts, impacting fields ranging from machine health monitoring to hands-free communication systems. Furthermore, ongoing work explores novel complex-valued representations and symmetries to enhance model efficiency and generalization.