Exploiting Redundancy: Separable Group Convolutional Networks on Lie Groups
This work addresses computational inefficiency in geometric deep learning for vision tasks, offering incremental improvements in model design.
The paper tackled parameter redundancy in group convolutional neural networks (G-CNNs) by introducing separable convolution kernels that share weights over subgroups, resulting in improved performance and computational efficiency across vision datasets, with Sim(2)-equivariant models further boosting performance.
Group convolutional neural networks (G-CNNs) have been shown to increase parameter efficiency and model accuracy by incorporating geometric inductive biases. In this work, we investigate the properties of representations learned by regular G-CNNs, and show considerable parameter redundancy in group convolution kernels. This finding motivates further weight-tying by sharing convolution kernels over subgroups. To this end, we introduce convolution kernels that are separable over the subgroup and channel dimensions. In order to obtain equivariance to arbitrary affine Lie groups we provide a continuous parameterisation of separable convolution kernels. We evaluate our approach across several vision datasets, and show that our weight sharing leads to improved performance and computational efficiency. In many settings, separable G-CNNs outperform their non-separable counterpart, while only using a fraction of their training time. In addition, thanks to the increase in computational efficiency, we are able to implement G-CNNs equivariant to the $\mathrm{Sim(2)}$ group; the group of dilations, rotations and translations. $\mathrm{Sim(2)}$-equivariance further improves performance on all tasks considered.