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Found 2 papers, 2 papers with code
Towards Principled Design of Deep Convolutional Networks: Introducing SimpNet
SimpNet outperforms the deeper and more complex architectures such as VGGNet, ResNet, WideResidualNet \etc, on several well-known benchmarks, while having 2 to 25 times fewer number of parameters and operations.
Ranked #118 on
Image Classification
on CIFAR-10
Lets keep it simple, Using simple architectures to outperform deeper and more complex architectures
Our simple 13-layer architecture outperforms most of the deeper and complex architectures to date such as VGGNet, ResNet, and GoogleNet on several well-known benchmarks while having 2 to 25 times fewer number of parameters and operations.
Ranked #11 on
Image Classification
on MNIST
