Design of kernels in convolutional neural networks for image classification

Zhun Sun, Mete Ozay, Takayuki Okatani

研究成果: Conference contribution

6 被引用数 (Scopus)


Despite the effectiveness of convolutional neural networks (CNNs) for image classification, our understanding of the effect of shape of convolution kernels on learned representations is limited. In this work, we explore and employ the relationship between shape of kernels which define receptive fields (RFs) in CNNs for learning of feature representations and image classification. For this purpose, we present a feature visualization method for visualization of pixel-wise classification score maps of learned features. Motivated by our experimental results, and observations reported in the literature for modeling of visual systems, we propose a novel design of shape of kernels for learning of representations in CNNs. In the experimental results, the proposed models also outperform the state-of-the-art methods employed on the CIFAR-10/100 datasets [1] for image classification. We also achieved an outstanding performance in the classification task, comparing to a base CNN model that introduces more parameters and computational time, using the ILSVRC-2012 dataset [2]. Additionally, we examined the region of interest (ROI) of different models in the classification task and analyzed the robustness of the proposed method to occluded images. Our results indicate the effectiveness of the proposed approach.

ホスト出版物のタイトルComputer Vision - 14th European Conference, ECCV 2016, Proceedings
編集者Bastian Leibe, Jiri Matas, Nicu Sebe, Max Welling
出版社Springer Verlag
出版ステータスPublished - 2016
イベント14th European Conference on Computer Vision, ECCV 2016 - Amsterdam, Netherlands
継続期間: 2016 10 82016 10 16


名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
9911 LNCS


Conference14th European Conference on Computer Vision, ECCV 2016

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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