  In this paper, we propose a simple and general approach to augment regular
convolution operator by injecting extra group-wise transformation during
training and recover it during inference. Extra transformation is carefully
selected to ensure it can be merged with regular convolution in each group and
will not change the topological structure of regular convolution during
inference. Compared with regular convolution operator, our approach (AugConv)
can introduce larger learning capacity to improve model performance during
training but will not increase extra computational overhead for model
deployment. Based on ResNet, we utilize AugConv to build convolutional neural
networks named AugResNet. Result on image classification dataset Cifar-10 shows
that AugResNet outperforms its baseline in terms of model performance.
