Image classification is an important part of pattern recognition. With the development of convolutional neural networks (CNNs), many CNN methods are proposed, which have a large number of samples for training, which can have high performance. However, there may exist limited samples in some real-world applications. In order to improve the performance of CNN learning with insufficient samples, this article proposes a new method called the classifier method based on a variational autoencoder (CFVAE), which is comprised of two parts: 1) a standard CNN as a prior classifier and 2) a CNN based on variational autoencoder (VAE) as a posterior classifier. First, the prior classifier is utilized to generate the prior label and information about distributions of latent variables; and the posterior classifier is trained to augment some latent variables from regularized distributions to improve the performance. Second, we also present the uniform objective function of CFVAE and put forward an optimization method based on the stochastic gradient variational Bayes method to solve the objective model. Third, we analyze the feasibility of CFVAE based on Hoeffding’s inequality and Chernoff’s bounding method. This analysis indicates that the latent variables augmentation method based on regularized latent variables distributions can generate samples fitting well with the distribution of data such that the proposed method can improve the performance of CNN with insufficient samples. Finally, the experiments manifest that our proposed CFVAE can provide more accurate performance than state-of-the-art methods.

中文翻译：

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一种训练样本不足的高效图像分类方法


图像分类是模式识别的重要组成部分。随着卷积神经网络（CNN）的发展，许多CNN方法被提出，这些方法有大量的样本进行训练，可以具有高性能。然而，在某些实际应用中可能存在有限的样本。为了提高样本不足时CNN学习的性能，本文提出了一种新方法，称为基于变分自编码器的分类器方法（CFVAE），该方法由两部分组成：1）标准CNN作为先验分类器，2） ）基于变分自动编码器（VAE）作为后验分类器的 CNN。首先，利用先验分类器生成先验标签和有关潜在变量分布的信息；后验分类器经过训练，可以从正则化分布中增加一些潜在变量，以提高性能。其次，我们还提出了CFVAE的统一目标函数，并提出了一种基于随机梯度变分贝叶斯方法的优化方法来求解目标模型。第三，我们基于Hoeffding不等式和Chernoff边界法分析了CFVAE的可行性。该分析表明，基于正则化潜变量分布的潜变量增强方法可以生成与数据分布很好地拟合的样本，因此该方法可以在样本不足的情况下提高 CNN 的性能。最后，实验表明我们提出的 CFVAE 可以提供比最先进的方法更准确的性能。