基于L2正则化的深度和经典表示协同加权图像分类
Collaboratively Weighting Deep and Classic Representation via L2 Regularization for Image Classification
摘要：
深度卷积神经网络为图像分类提供了强大的特征学习能力。深度图像特征可以用于处理许多图像理解任务，如图像分类和对象识别。然而，在一个数据集中获得的鲁棒性在另一个域中很难再现，这导致效率低下的模型远离最先进的。我们提出了一种深度协作基于权重的分类（DeepCWC）方法来解决这个问题，通过提供一种新的选择，以充分利用经典机器学习中的深度特征。它首先对原始图像进行基于L2范数的协同表示，以及由深度CNN模型提取的深度特征。然后，两个距离向量，获得的线性表示对的基础上，通过一种新的合作权重融合在一起。这种协作权重使深度和经典表示能够相互权衡。我们在10个面部和对象数据集上进行了一系列实验，观察到两种表示之间的互补性。提出的DeepCWC产生了非常有前途的分类结果，并且优于许多其他基准方法，特别是Fashion-MNIST声称的方法。代码将发布在我们的公共存储库中。
英文摘要：
Deep convolutional neural networks provide a powerful feature learning capability for image classification. The deep image features can be utilized to deal with many image understanding tasks like image classification and object recognition. However, the robustness obtained in one dataset can be hardly reproduced in the other domain, which leads to inefficient models far from state-of-the-art. We propose a deep collaborative weight-based classification (DeepCWC) method to resolve this problem, by providing a novel option to fully take advantage of deep features in classic machine learning. It firstly performs the L2-norm based collaborative representation on the original images, as well as the deep features extracted by deep CNN models. Then, two distance vectors, obtained based on the pair of linear representations, are fused together via a novel collaborative weight. This collaborative weight enables deep and classic representations to weigh each other. We observed the complementarity between two representations in a series of experiments on 10 facial and object datasets. The proposed DeepCWC produces very promising classification results, and outperforms many other benchmark methods, especially the ones claimed for Fashion-MNIST. The code is going to be published in our public repository.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
