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31 January 2020Transfer of a high-level knowledge in HoughNet neural network
1Federal Research Ctr. “Computer Science and Control” (Russian Federation) 2Smart Engines Service LLC (Russian Federation) 3Institute for Information Transmission Problems (Kharkevich Institute) (Russian Federation) 4Moscow Institute of Physics and Technology (Russian Federation)
In this paper, we study the recently introduced neural network architecture HoughNet for the ability to accumulate transferable high-level features. The main idea of that neural network is to use convolutional layers separated with Fast Hough Transform layers to enable an analysis of complex non-linear statistics along different lines. We show that different convolutional blocks in this neural network have essentially different purposes. While initial features extracting is task-specific, the main part of the neural network operates with high-level features and do not require re-training in order to be applied to data from a different domain. To prove our statement, we two sets of the images with different origins and demonstrate Transfer Learning presence in the neural network except for the first layers which are highly task-specific.
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Alexander V. Sheshkus, Dmitry Nikolaev, "Transfer of a high-level knowledge in HoughNet neural network," Proc. SPIE 11433, Twelfth International Conference on Machine Vision (ICMV 2019), 1143322 (31 January 2020); https://doi.org/10.1117/12.2559454