# ExpandNets: Linear Over-parameterization to Train Compact Convolutional Networks Source: [ExpandNets: Linear Over-parameterization to Train Compact Convolutional Networks]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:06:39.153Z Authors: Shuxuan Guo, Jose M. Alvarez, Mathieu Salzmann Published: 2018-11-26T16:40:24.000Z ## Abstract We introduce an approach to training a given compact network. To this end, we leverage over-parameterization, which typically improves both neural network optimization and generalization. Specifically, we propose to expand each linear layer of the compact network into multiple consecutive linear layers, without adding any nonlinearity. As such, the resulting expanded network, or ExpandNet, can be contracted back to the compact one algebraically at inference. In particular, we introduce two convolutional expansion strategies and demonstrate their benefits on several tasks, including image classification, object detection, and semantic segmentation. As evidenced by our experiments, our approach outperforms both training the compact network from scratch and performing knowledge distillation from a teacher. Furthermore, our linear over-parameterization empirically reduces gradient confusion during training and improves the network generalization. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Image Classification on ImageNet (val) (Top-1/Top-5 Accuracy)](): Image Classification; ImageNet (val); Top-1 Accuracy: 69.4. ## Original sources and implementation [Original paper]() [PDF]()