PCANet and its variants provided good accuracy results for classification tasks. However, despite the importance of network depth in achieving good classification accuracy, these networks were trained with a maximum of nine layers. In this paper, we introduce a residual compensation convolutional network, which is the first PCANet-like network trained with hundreds of layers while improving classification accuracy. The design of the proposed network consists of several convolutional layers, each followed by post-processing steps and a classifier. To correct the classification errors and significantly increase the network's depth, we train each layer with new labels derived from the residual information of all its preceding layers. This learning mechanism is accomplished by traversing the network's layers in a single forward pass without backpropagation or gradient computations. Our experiments on four distinct classification benchmarks (MNIST, CIFAR-10, CIFAR-100, and TinyImageNet) show that our deep network outperforms all existing PCANet-like networks and is competitive with several traditional gradient-based models.

翻译：PCANet及其变体在分类任务中取得了良好的准确率。然而，尽管网络深度对实现高分类精度至关重要，这类网络的最大训练层数仅为九层。本文提出了一种残差补偿卷积网络，这是首个采用数百层训练且能提升分类精度的类PCANet网络。该网络的设计包含多个卷积层，每层后接后处理步骤与分类器。为修正分类误差并显著增加网络深度，我们利用所有前置层的残差信息生成新标签来训练每个层级。这一学习机制通过单次前向传播遍历网络层级实现，无需反向传播或梯度计算。我们在四个不同分类基准（MNIST、CIFAR-10、CIFAR-100和TinyImageNet）上的实验表明，该深层网络性能超越所有现有类PCANet网络，并与多种传统梯度下降模型具有竞争力。