The paper "Learning Efficient Convolutional Networks through Network Slimming" addresses the challenge of deploying deep convolutional neural networks (CNNs) in resource-constrained environments by proposing a novel learning scheme called network slimming. This scheme aims to reduce the model size, decrease run-time memory footprint, and lower the number of computing operations without compromising accuracy. The method enforces channel-level sparsity in the network by applying L1 regularization to the scaling factors in batch normalization layers during training. These scaling factors are used to identify and prune insignificant channels, resulting in a more compact and efficient model. The approach is simple, effective, and can be applied to modern CNN architectures without requiring special software or hardware accelerators. Empirical results on various image classification datasets, including VGGNet, ResNet, and DenseNet, demonstrate that network slimming can achieve up to 20× reduction in model size and 5× reduction in computing operations while maintaining or improving accuracy. The paper also introduces a multi-pass scheme to further enhance the compression rate and discusses the handling of cross-layer connections and pre-activation structures in modern CNNs.The paper "Learning Efficient Convolutional Networks through Network Slimming" addresses the challenge of deploying deep convolutional neural networks (CNNs) in resource-constrained environments by proposing a novel learning scheme called network slimming. This scheme aims to reduce the model size, decrease run-time memory footprint, and lower the number of computing operations without compromising accuracy. The method enforces channel-level sparsity in the network by applying L1 regularization to the scaling factors in batch normalization layers during training. These scaling factors are used to identify and prune insignificant channels, resulting in a more compact and efficient model. The approach is simple, effective, and can be applied to modern CNN architectures without requiring special software or hardware accelerators. Empirical results on various image classification datasets, including VGGNet, ResNet, and DenseNet, demonstrate that network slimming can achieve up to 20× reduction in model size and 5× reduction in computing operations while maintaining or improving accuracy. The paper also introduces a multi-pass scheme to further enhance the compression rate and discusses the handling of cross-layer connections and pre-activation structures in modern CNNs.