Conventional application of convolutional neural networks (CNNs) for image classification and recognition is based on the assumption that all target classes are equal(i.e., no hierarchy) and exclusive of one another (i.e., no overlap). CNN-based image classifiers built on this assumption, therefore, cannot take into account an innate hierarchy among target classes (e.g., cats and dogs in animal image classification) or additional information that can be easily derived from the data (e.g.,numbers larger than five in the recognition of handwritten digits), thereby resulting in scalability issues when the number of target classes is large. Combining two related but slightly different ideas of hierarchical classification and logical learning by auxiliary inputs, we propose a new learning framework called hierarchical auxiliary learning, which not only address the scalability issues with a large number of classes but also could further reduce the classification recognition errors with a reasonable number of classes. In the hierarchical auxiliary learning, target classes are semantically or non-semantically grouped into superclasses, which turns the original problem of mapping between an image and its target class into a new problem of mapping between a pair of an image and its superclass and the target class. To take the advantage of superclasses, we introduce an auxiliary block into a neural network, which generates auxiliary scores used as additional information for final classification recognition; in this paper, we add the auxiliary block between the last residual block and the fully-connected output layer of the ResNet. Experimental results demonstrate that the proposed hierarchical auxiliary learning can reduce classification errors up to 0.56, 1.6 and 3.56 percent with MNIST, SVHN and CIFAR-10 datasets, respectively.
There have been proposed numerous approaches to utilize hierarchical class information as well. connect multi-layer perceptrons (MLPs) and let each MLP sequentially learn a hierarchical class as rear layer takes the output of the preceding layer as its input. insert coarse category component and fine category component after a shared layer. Classes are classified into K-coarse categories, and K-fine category components are targeted at each coarse category. In @cite_9 , CNN learns label generated by maximum margin clustering at root node, and images in the same cluster are classified at leaf node.
@cite_9
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The availability of large-scale annotated data and the uneven separability of different data categories have become two major impediments of deep learning for image classification. In this paper, we present a semi-supervised hierarchical convolutional neural network (SS-HCNN) to address these two challenges. A large-scale unsupervised maximum margin clustering technique is designed, which splits images into a number of hierarchical clusters iteratively to learn cluster-level CNNs at parent nodes and category-level CNNs at leaf nodes. The splitting uses the similarity of CNN features to group visually similar images into the same cluster, which relieves the uneven data separability constraint. With the hierarchical cluster-level CNNs capturing certain high-level image category information, the category-level CNNs can be trained with a small amount of labeled images, and this relieves the data annotation constraint. A novel cluster splitting criterion is also designed, which automatically terminates the image clustering in the tree hierarchy. The proposed SS-HCNN has been evaluated on the CIFAR-100 and ImageNet classification datasets. The experiments show that the SS-HCNN trained using a portion of labeled training images can achieve comparable performance with other fully trained CNNs using all labeled images. Additionally, the SS-HCNN trained using all labeled images clearly outperforms other fully trained CNNs.