基于图像插值的小样本手写数字识别研究

Image Interpolation-Based Few-Shot Learning of Handwritten Digit Recognition

人工智能方法的高性能通常需要有充足的数据来训练模型参数。如何在数据量不足的情况下提升模型的性能,即小样本学习,是人工智能领域的重要研究方向之一。本文提出了基于图像插值的小样本学习策略,并在手写数字图像识别任务中验证了该策略的可行性。系统研究了全连接神经网络和卷积神经网络对MNIST和USPS手写数字图像识别的小样本学习性能。计算结果表明,基于图像插值的数据增强方法可以显著提升神经网络在小样本数据中的特征提取能力和学习效率,且选择合适的图像插值缩放系数可以进一步优化神经网络的小样本学习性能。

The high performance of artificial intelligence(AI)is usually dependent on large and sufficient data to train parameters.How to improve the predictive performance in the case of insufficient data,i.e.,few-shot learning,is one of the important research subjects in the AI field.An image interpolation-based few-shot learning strategy is proposed,whose feasibility is verified in the task of handwritten digit image recognition.The few-shot learning performance of dense neural network and convolutional neural network in MNIST and USPS handwritten digit image recognition is systematically studied.The calculation results show that the image interpolation-based data enhancement method can evidently promote the characteristics extraction ability and learning efficiency of neural network in small sample data.Moreover,selecting the appropriate scaling coefficient of image interpolation can further optimize the few-shot learning performance of neural network.