基于小样本学习融合随机深度和多尺度卷积的SDM-RNET网络

针对神经网络难以利用少量标注数据获取足够的信息来正确分类图像的问题, 提出了一种融合随机深度网络和多尺度卷积的关系网络——SDM-RNET. 首先在模型嵌入模块引入随机深度网络用于加深模型深度, 然后在特征提取阶段采用多尺度深度可分离卷积替代普通卷积进行特征融合, 经过骨干网络后再采用深浅层特征融合获取更丰富的图像特征, 最终学习预测出图像的类别. 在mini-ImageNet、RP2K、Omniglot这3个数据集上对比该方法与其他小样本图像分类方法, 结果表明在5-way 1-shot和5-way 5-shot分类任务上该方法准确率最高.

SDM-RNET Network Based on Small-sample Learning Fusing Stochastic Depth and Multi-scale Convolution

To solve the problem that it is difficult for neural networks to obtain enough information to correctly classify images by using a small amount of labeled data, this study proposes a new relational network, SDM-RNET, which combines random deep network and multi-scale convolution. First, a stochastic deep network is introduced into the model embedding module to deepen the model depth. Then, in the feature extraction stage, multi-scale depth-separable convolution is adopted to replace ordinary convolution for feature fusion. After the backbone network, deep and shallow layer feature fusion is applied to obtain richer image features and finally learn to predict the categories of images. Compared with other small sample image classification methods on mini-ImageNet, RP2K, and Omniglot datasets, the results show that the proposed method has the highest accuracy on 5-way 1-shot and 5-way 5-shot classification tasks.