基于多分辨率自蒸馏网络的小样本图像分类

因图像数据具有大量的空间冗余信息，传统的多分辨率网络在处理图像数据时会产生较高的计算成本。而自蒸馏学习方法能够在精度与计算成本之间实现动态平衡，使模型在不增加网络深度和宽度的基础上，有效地提高模型精度。提出一种多分辨率自蒸馏网络（MRSDN），用于解决小样本学习中输入样本的空间冗余问题。从原始网络中分出一个浅层子网络以识别图像的低分辨率表示，并且保持该原始网络识别高分辨率图像特征的能力。同时，在多分辨率网络中引入改进的全局注意力机制，以减少信息损失且放大全局交互表示。利用自蒸馏学习方法将网络中更深层的知识压缩到浅层子网络中，以提升浅层子网络的泛化能力。在此基础上，将低分辨率网络中的粗粒度特征融合到高分辨率网络中，从而提高模型提取图像特征的能力。实验结果表明，在Mini-ImageNet数据集上MRSDN网络对5-way 1-shot与5-way 5-shot任务的准确率分别为56.34%和74.35%，在Tiered-ImageNet数据集上对5-way 1-shot与5-way 5-shot任务的准确率分别为59.56%和78.96%，能有效缓解高分辨率图像输入时的空间冗余问题，提高小样本图像分类的准确率。

Few-Shot Image Classification Based on Multi-Resolution Self-Distillation Network

The traditional multi-resolution network incurs high computing costs when processing image data owing to a large amount of spatial redundancy information in the image data.The Self-Distillation(SD) learning method can achieve a dynamic balance between accuracy and calculation cost, effectively improving the accuracy of the model without increasing the depth and width of the network.A Multi-Resolution Self-Distillation Network(MRSDN) is proposed to solve the spatial redundancy of input samples in Few-Shot Learning(FSL).A shallow sub-network is separated from the original network to recognize the low-resolution representation of the image, and the ability of the original network to recognize the high-resolution image features is maintained.In addition, an improved Global Attention Mechanism(GAM) is introduced into the multi-resolution network to reduce information loss and enlarge the global interactive representation.The SD learning method is used to compress the in-depth knowledge of the network into a shallow sub-network to improve the generalization ability of the shallow sub-network.The coarse granularity features in the low-resolution network are fused into the high-resolution network to improve the ability of the model to extract image features.The experimental results show that accuracy of the MRSDN network for five-way one-shot and five-way five-shot tasks on the Mini-ImageNet dataset are 56.34% and 74.35%, respectively.The accuracy of the network for five-way one-shot and five-way five-shot tasks on the Tiered-ImageNet dataset are 59.56% and 78.96%, respectively.The proposed network can effectively alleviate the spatial redundancy when inputting high-resolution images, improving the accuracy of few-shot image classification.