Open AccessGDC block: gradient-guided direction-aware convolution block for image classification
Author(s) -
Jinye Peng,
Maomei Liu,
Lei Tang,
Sheng Zhong,
Hangzai Luo
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1848/1/012088
Subject(s) - convolution (computer science) , block (permutation group theory) , computer science , convolutional neural network , artificial intelligence , field (mathematics) , image (mathematics) , scheme (mathematics) , pattern recognition (psychology) , algorithm , computer vision , artificial neural network , mathematics , mathematical analysis , geometry , pure mathematics
Convolutional Neural Network (CNN) has achieved great success in visual applications. In the field of image classification, researchers usually customize CNN models to meet the needs of different real-world applications. It consumes a lot of human labor and computing resources but only achieves slight performance improvement. Besides, some recent works try to integrate prior knowledge into classic CNN models to improve their accuracy, but they are usually only effective for special applications. In this paper, we propose a gradient-guided direction-aware convolution (GDC) block. It can be used to replace low-level convolutions of existing CNN without changing the off-the-shelf architecture. The gradient priors provide object shapes that CNN’s low-level convolution requires. And the direction-aware mechanism expands the receptive field size. This scheme is a trade-off between model size and model accuracy. Experimental results show it can moderately reduce the size of any CNN models while enhancing their performance.