Abstract
Group convolution has been widely used in deep learning community to achieve computation efficiency. In this paper, we develop CondenseNet-elasso to eliminate feature correlation among different convolution groups and alleviate neural network's overfitting problem. It applies exclusive lasso regularization on CondenseNet. The exclusive lasso regularizer encourages different convolution groups to use different subsets of input channels therefore learn more diversified features. Our experiment results on CIFAR10, CIFAR100 and Tiny ImageNet show that CondenseNets-elasso are more efficient than CondenseNets and other DenseNet' variants.