论文标题
Augop：将转化为神经操作员
AugOp: Inject Transformation into Neural Operator
论文作者
论文摘要
在本文中，我们提出了一种简单而通用的方法来增强常规卷积操作员，通过在训练过程中注入额外的小组转换，并在推断过程中恢复它。精心选择额外的转换，以确保可以在每组中定期卷积合并，并且不会改变推断期间常规卷积的拓扑结构。与常规的卷积操作员相比，我们的方法（AUGCONV）可以引入更大的学习能力，以提高训练期间的模型性能，但不会增加额外的计算开销来进行模型部署。根据重新连接，我们利用AugConv构建名为Augresnet的卷积神经网络。图像分类数据集CIFAR-10上的结果表明，Augresnet在模型性能方面的表现优于其基线。
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.
