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Email Address. Sign In. SS-HCNN: Semi-Supervised Hierarchical Convolutional Neural Network for Image Classification Abstract: 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.

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Article :. Date of Publication: 15 January DOI: Sponsored by: IEEE. Need Help? SS-HCNN: Semi-Supervised Hierarchical Convolutional Neural Network for Image Classification Abstract: 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 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.

Article :. Date of Publication: 14 December

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