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Compressing deep quaternion neural networks with targeted regularization
2020
CAAI Transactions on Intelligence Technology
In recent years, hyper-complex deep networks (such as complex-valued and quaternion-valued neural networks -QVNNs) have received a renewed interest in the literature. They find applications in multiple fields, ranging from image reconstruction to 3D audio processing. Similar to their real-valued counterparts, quaternion neural networks require custom regularisation strategies to avoid overfitting. In addition, for many real-world applications and embedded implementations, there is the need of
doi:10.1049/trit.2020.0020
fatcat:aqwj6xzfqvcsbkhzauku7j26e4