Deep learning seeks to achieve excellent performance for representation learning in image datasets. However, supervised deep learning models such as convolutional neural networks require a large number of labeled image data, which is intractable in applications, while unsupervised deep learning models like stacked denoising auto-encoder cannot employ label information. Meanwhile, the redundancy of image data incurs performance degradation on representation learning for aforementioned models. To address these problems, we propose a semi-supervised deep learning framework called stacked convolutional sparse auto-encoder, which can learn robust and sparse representations from image data with fewer labeled data records. More specifically, the framework is constructed by stacking layers. In each layer, higher layer feature representations are generated by features of lower layers in a convolutional way with kernels learned by a sparse auto-encoder. Meanwhile, to solve the data redundance problem, the algorithm of Reconstruction Independent Component Analysis is designed to train on patches for sphering the input data. The label information is encoded using a Softmax Regression model for semi-supervised learning. With this framework, higher level representations are learned by layers mapping from image data. It can boost the performance of the base subsequent classifiers such as support vector machines. Extensive experiments demonstrate the superior classification performance of our framework compared to several state-of-the-art representation learning methods.

中文翻译：

---

用于表示学习的堆叠卷积稀疏自动编码器

深度学习旨在为图像数据集中的表示学习实现出色的性能。然而，有监督的深度学习模型（如卷积神经网络）需要大量标记的图像数据，这在应用中难以处理，而无监督的深度学习模型（如堆叠去噪自编码器）则无法使用标签信息。同时，图像数据的冗余会导致上述模型的表示学习性能下降。为了解决这些问题，我们提出了一种称为堆叠卷积稀疏自动编码器的半监督深度学习框架，它可以从具有较少标记数据记录的图像数据中学习鲁棒和稀疏的表示。更具体地说，框架是通过堆叠层来构建的。在每一层，较高层的特征表示是由较低层的特征以卷积方式与稀疏自动编码器学习的内核生成的。同时，为了解决数据冗余问题，设计了重构独立分量分析算法，对输入数据进行球化训练。标签信息使用用于半监督学习的 Softmax 回归模型进行编码。使用此框架，通过从图像数据映射的层来学习更高级别的表示。它可以提高基础后续分类器（例如支持向量机）的性能。与几种最先进的表示学习方法相比，大量实验证明了我们框架的卓越分类性能。