https://doi.org/10.1140/epjs/s11734-024-01373-2
Regular Article
Dynamic convolution for image matching
1
National Research University Higher School of Economics, 25/12 Bol’shaya Pecherskaya Street, 603155, Nizhny Novgorod, Russia
2
A. V. Gaponov-Grekhov Institute of Applied Physics of the Russian Academy of Sciences, 46 Ulyanova Street, 603950, Nizhny Novgorod, Russia
Received:
24
July
2024
Accepted:
10
October
2024
Published online:
29
October
2024
Convolution neural networks (CNNs) are omnipresent in modern computer vision models and also widely used in other tasks such as voice recognition, time series analysis, machine translation, etc. In the present paper, we introduce a novel architecture of CNNs using dynamic convolutions in which the kernels are generated based on the input data. We apply this architecture to the image matching problem and develop a two-branch network in which one branch generates kernels used in convolutional layers of the other branch. We test our model on a canonical MNIST benchmark and demonstrate that it shows faster learning and better performance than the baseline model with standard convolutions. Potential applications of our architecture includes numerous problems in image analysis, time series forecasting, physical-informed machine learning, etc.
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