论文标题
当字典学习达到深度学习时：深度词典学习和编码网络，用于图像识别有限的数据
When Dictionary Learning Meets Deep Learning: Deep Dictionary Learning and Coding Network for Image Recognition with Limited Data
论文作者
论文摘要
我们提出了一个新的深层词典学习和编码网络（DDLCN），用于具有有限数据的图像识别任务。所提出的DDLCN具有大多数标准的深度学习层（例如，输入/输出，合并，完全连接等），但是基本的卷积层被我们提出的复合词典学习和编码层所取代。词典学习学习了输入培训数据过度完整的词典。在深层编码层，添加了局部性约束，以确保激活的词典相互接近。然后将激活的词典原子组装并传递到复合词典学习和编码层。这样，第一层中的激活原子可以由第二个词典中的较深原子表示。直觉上，第二个词典旨在学习输入词典原子中共享的细颗粒组件，因此可以获得词典原子的更有信息和歧视性的低级表示。我们从经验上将DDLCN与几种领先的词典学习方法和深度学习模型进行了比较。五个流行数据集的实验结果表明，与训练数据受到限制时，DDLCN与最先进的方法相比，取得了竞争性结果。代码可在https://github.com/ha0tang/ddlcn上找到。
We present a new Deep Dictionary Learning and Coding Network (DDLCN) for image recognition tasks with limited data. The proposed DDLCN has most of the standard deep learning layers (e.g., input/output, pooling, fully connected, etc.), but the fundamental convolutional layers are replaced by our proposed compound dictionary learning and coding layers. The dictionary learning learns an over-complete dictionary for input training data. At the deep coding layer, a locality constraint is added to guarantee that the activated dictionary bases are close to each other. Then the activated dictionary atoms are assembled and passed to the compound dictionary learning and coding layers. In this way, the activated atoms in the first layer can be represented by the deeper atoms in the second dictionary. Intuitively, the second dictionary is designed to learn the fine-grained components shared among the input dictionary atoms, thus a more informative and discriminative low-level representation of the dictionary atoms can be obtained. We empirically compare DDLCN with several leading dictionary learning methods and deep learning models. Experimental results on five popular datasets show that DDLCN achieves competitive results compared with state-of-the-art methods when the training data is limited. Code is available at https://github.com/Ha0Tang/DDLCN.
