Learning unfolded networks with a cyclic group structure
This work addresses the need for more interpretable and efficient neural networks in computer vision, though it is incremental as it builds on existing equivariant and unfolded network frameworks.
The paper tackled the problem of incorporating domain knowledge into deep neural networks by proposing equivariant unfolded networks that explicitly encode rotation equivariance, achieving competitive performance with fewer parameters on rotated MNIST and CIFAR-10 datasets.
Deep neural networks lack straightforward ways to incorporate domain knowledge and are notoriously considered black boxes. Prior works attempted to inject domain knowledge into architectures implicitly through data augmentation. Building on recent advances on equivariant neural networks, we propose networks that explicitly encode domain knowledge, specifically equivariance with respect to rotations. By using unfolded architectures, a rich framework that originated from sparse coding and has theoretical guarantees, we present interpretable networks with sparse activations. The equivariant unfolded networks compete favorably with baselines, with only a fraction of their parameters, as showcased on (rotated) MNIST and CIFAR-10.