论文标题
通过保守的现场正规化和集成改善卷积神经网络
Improving Convolutional Neural Networks Via Conservative Field Regularisation and Integration
论文作者
论文摘要
当前关于卷积神经网络（CNN）的研究主要集中于改变网络的体系结构，优化超参数并改善梯度下降。但是，大多数工作仅使用CNN内部的3个标准操作系列，卷积，激活函数和合并。在这项工作中，我们根据Laplacian的绿色功能提出了一个新的操作系列，该家族允许网络解决Laplacian，可以整合任何向量领域并通过强迫其保守来整合该领域并正规化该领域。因此，绿色的功能（GF）是第一个通过迫使其保守和物理解释的操作，而不是正规化权重标准来正规化2D或3D特征空间。我们的结果表明，这种正则化使网络可以更快地学习，具有更平滑的训练曲线并更好地概括，而无需任何其他参数。当前的手稿提出了早期结果，需要更多的工作来基准提出的方法。
Current research in convolutional neural networks (CNN) focuses mainly on changing the architecture of the networks, optimizing the hyper-parameters and improving the gradient descent. However, most work use only 3 standard families of operations inside the CNN, the convolution, the activation function, and the pooling. In this work, we propose a new family of operations based on the Green's function of the Laplacian, which allows the network to solve the Laplacian, to integrate any vector field and to regularize the field by forcing it to be conservative. Hence, the Green's function (GF) is the first operation that regularizes the 2D or 3D feature space by forcing it to be conservative and physically interpretable, instead of regularizing the norm of the weights. Our results show that such regularization allows the network to learn faster, to have smoother training curves and to better generalize, without any additional parameter. The current manuscript presents early results, more work is required to benchmark the proposed method.
