TY - GEN
T1 - A Trainable Multiplication Layer for Auto-correlation and Co-occurrence Extraction
AU - Hayashi, Hideaki
AU - Uchida, Seiichi
N1 - Funding Information:
Supported by Qdai-jump Research Program and JSPS KAKENHI Grant JP17K12752 and JP17K19402.
Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - In this paper, we propose a trainable multiplication layer (TML) for a neural network that can be used to calculate the multiplication between the input features. Taking an image as an input, the TML raises each pixel value to the power of a weight and then multiplies them, thereby extracting the higher-order local auto-correlation from the input image. The TML can also be used to extract co-occurrence from the feature map of a convolutional network. The training of the TML is formulated based on backpropagation with constraints to the weights, enabling us to learn discriminative multiplication patterns in an end-to-end manner. In the experiments, the characteristics of the TML are investigated by visualizing learned kernels and the corresponding output features. The applicability of the TML for classification and neural network interpretation is also evaluated using public datasets.
AB - In this paper, we propose a trainable multiplication layer (TML) for a neural network that can be used to calculate the multiplication between the input features. Taking an image as an input, the TML raises each pixel value to the power of a weight and then multiplies them, thereby extracting the higher-order local auto-correlation from the input image. The TML can also be used to extract co-occurrence from the feature map of a convolutional network. The training of the TML is formulated based on backpropagation with constraints to the weights, enabling us to learn discriminative multiplication patterns in an end-to-end manner. In the experiments, the characteristics of the TML are investigated by visualizing learned kernels and the corresponding output features. The applicability of the TML for classification and neural network interpretation is also evaluated using public datasets.
UR - http://www.scopus.com/inward/record.url?scp=85067357575&partnerID=8YFLogxK
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U2 - 10.1007/978-3-030-20890-5_27
DO - 10.1007/978-3-030-20890-5_27
M3 - Conference contribution
AN - SCOPUS:85067357575
SN - 9783030208899
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 414
EP - 430
BT - Computer Vision - ACCV 2018 - 14th Asian Conference on Computer Vision, Revised Selected Papers
A2 - Jawahar, C.V.
A2 - Li, Hongdong
A2 - Schindler, Konrad
A2 - Mori, Greg
PB - Springer Verlag
T2 - 14th Asian Conference on Computer Vision, ACCV 2018
Y2 - 2 December 2018 through 6 December 2018
ER -