关键词: 卷积神经网络, 三生损失, 残差学习, 挑战性样本采样, 样本中心点

Abstract: For multi-classification image tasks, a residual triplet network based on deep convolution is proposed, which aims to train neural networks to obtain useful feature representations through residual learning and distance comparison. Firstly, a 21-layer deep convolution neural network is designed as the embedded network of the triplet network, where the convolutional network is connected with 6 blocks. By using residual learning, the output of each block is combined with the input of this block and the output of the convolutional layer which focus on reducing the difficulty of network learning and avoiding degradation. Then, each block employed the convolution layers with the same topological branch to broaden the width of the network.Finally, to enhance the transfer of feature information, the fully-connected layer concatenated the output of the previous convolutional layers and blocks. Before training, the cross-combined sampling method is used to increase effective samples for hard samples. During training, using the sample center point to replace the anchor sample as an input can reduce the error rate by 0.5% on average. Among the triplet network series, we achieved the best results on the MNIST, CIFAR10, and SVHN. In all classification networks, we achieved the best results on the MNIST and performed well on CIFAR10 and SVHN.

Key words: convolution neural network, triplet loss, residual learning, hard sample mining, sample center point