Deep learning has revolutionized the computer vision and image classification domains. In this context Convolutional Neural Networks (CNNs) based architectures are the most widely applied models. In this article, we introduced two procedures for training Convolutional Neural Networks (CNNs) and Deep Neural Network based on Gradient Boosting (GB), namely GB-CNN and GB-DNN. These models are trained to fit the gradient of the loss function or pseudo-residuals of previous models. At each iteration, the proposed method adds one dense layer to an exact copy of the previous deep NN model. The weights of the dense layers trained on previous iterations are frozen to prevent over-fitting, permitting the model to fit the new dense as well as to fine-tune the convolutional layers (for GB-CNN) while still utilizing the information already learned. Through extensive experimentation on different 2D-image classification and tabular datasets, the presented models show superior performance in terms of classification accuracy with respect to standard CNN and Deep-NN with the same architecture.

翻译：深度学习已彻底改变了计算机视觉与图像分类领域。在此背景下，基于卷积神经网络（CNN）的架构成为应用最广泛的模型。本文提出了两种基于梯度提升（GB）的训练卷积神经网络（CNN）与深度神经网络（DNN）的方法，即GB-CNN与GB-DNN。这些模型被训练以拟合损失函数的梯度（即前序模型的伪残差）。在每次迭代中，所提方法向前一个深度神经网络模型的精确副本中添加一个全连接层。前序迭代中训练的全连接层权重被冻结以防止过拟合，从而允许模型在拟合新全连接层的同时微调卷积层（针对GB-CNN），并充分利用已学到的信息。通过在多个二维图像分类与表格数据集上的大量实验，所提出的模型在分类准确率方面相较于相同架构的标准CNN与深度神经网络表现出更优性能。