CASIA OpenIR  > 智能感知与计算研究中心
Irregular Convolutional Neural Networks
Ma JB(马佳彬); Wang W(王威); Wang L(王亮)
2017-11
Conference NameAsian Conference on Pattern Recognition (ACPR)
Conference DateNovember 26-29 2017
Conference PlaceNanjing, China
AbstractConvolutional kernels are basic and vital components of deep Convolutional Neural Networks (CNN). In this paper, we equip convolutional kernels with shape attributes to generate the deep Irregular Convolutional Neural Networks (ICNN). Compared to traditional CNN applying regular convolutional kernels like 3 × 3, our approach trains irregular kernel shapes to better fit the geometric variations of input features. In other words, shapes are learnable parameters in addition to weights. The kernel shapes and weights are learned simultaneously during end-to-end training with the standard back-propagation algorithm. Experiments for semantic segmentation are implemented to validate the effectiveness of our proposed ICNN
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Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20658
Collection智能感知与计算研究中心
Affiliation中国科学院自动化研究所
Recommended Citation
GB/T 7714
Ma JB,Wang W,Wang L. Irregular Convolutional Neural Networks[C],2017.
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