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Found 4 papers, 1 papers with code
Deep Residual Axial Networks
The axial CNNs are predicated on the assumption that the dataset supports approximately separable convolution operations with little or no loss of training accuracy.
Deep Axial Hypercomplex Networks
We conduct experiments on CIFAR benchmarks, SVHN, and Tiny ImageNet datasets and achieve better performance with fewer trainable parameters and FLOPS.
Enhancing ResNet Image Classification Performance by using Parameterized Hypercomplex Multiplication
Recently, many deep networks have introduced hypercomplex and related calculations into their architectures.
Improving Axial-Attention Network Classification via Cross-Channel Weight Sharing
In recent years, hypercomplex-inspired neural networks (HCNNs) have been used to improve deep learning architectures due to their ability to enable channel-based weight sharing, treat colors as a single entity, and improve representational coherence within the layers.
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