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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 32

Training CNNs With Normalized Kernels

March 15, 2023

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Published Date: 2018-02-08

Registration: ISSN 2374-3468 (Online) ISSN 2159-5399 (Print)

Copyright: Published by AAAI Press, Palo Alto, California USA Copyright © 2018, Association for the Advancement of Artificial Intelligence All Rights Reserved.

Authors

Mete Ozay

Tohoku University


Takayuki Okatani

Tohoku University and RIKEN Center for AIP


DOI:

10.1609/aaai.v32i1.11624


Abstract:

Several methods of normalizing convolution kernels have been proposed in the literature to train convolutional neural networks (CNNs), and have shown some success. However, our understanding of these methods has lagged behind their success in application; there are a lot of open questions, such as why a certain type of kernel normalization is effective and what type of normalization should be employed for each (e.g., higher or lower) layer of a CNN. As the first step towards answering these questions, we propose a framework that enables us to use a variety of kernel normalization methods at any layer of a CNN. A naive integration of kernel normalization with a general optimization method, such as SGD, often entails instability while updating parameters. Thus, existing methods employ ad-hoc procedures to empirically assure convergence. In this study, we pose estimation of convolution kernels under normalization constraints as constraint-free optimization on kernel submanifolds that are identified by the employed constraints. Note that naive application of the established optimization methods for matrix manifolds to the aforementioned problems is not feasible because of the hierarchical nature of CNNs. To this end, we propose an algorithm for optimization on kernel manifolds in CNNs by appropriate scaling of the space of kernels based on structure of CNNs and statistics of data. We theoretically prove that the proposed algorithm has assurance of almost sure convergence to a solution at single minimum. Our experimental results show that the proposed method can successfully train popular CNN models using several different types of kernel normalization methods. Moreover, they show that the proposed method improves classification performance of baseline CNNs, and provides state-of-the-art performance for major image classification benchmarks.

Topics: AAAI

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Mete Ozay||Takayuki Okatani Training CNNs With Normalized Kernels Proceedings of the AAAI Conference on Artificial Intelligence, 32 (2018) .

Mete Ozay||Takayuki Okatani Training CNNs With Normalized Kernels AAAI 2018, .

Mete Ozay||Takayuki Okatani (2018). Training CNNs With Normalized Kernels. Proceedings of the AAAI Conference on Artificial Intelligence, 32, .

Mete Ozay||Takayuki Okatani. Training CNNs With Normalized Kernels. Proceedings of the AAAI Conference on Artificial Intelligence, 32 2018 p..

Mete Ozay||Takayuki Okatani. 2018. Training CNNs With Normalized Kernels. "Proceedings of the AAAI Conference on Artificial Intelligence, 32". .

Mete Ozay||Takayuki Okatani. (2018) "Training CNNs With Normalized Kernels", Proceedings of the AAAI Conference on Artificial Intelligence, 32, p.

Mete Ozay||Takayuki Okatani, "Training CNNs With Normalized Kernels", AAAI, p., 2018.

Mete Ozay||Takayuki Okatani. "Training CNNs With Normalized Kernels". Proceedings of the AAAI Conference on Artificial Intelligence, 32, 2018, p..

Mete Ozay||Takayuki Okatani. "Training CNNs With Normalized Kernels". Proceedings of the AAAI Conference on Artificial Intelligence, 32, (2018): .

Mete Ozay||Takayuki Okatani. Training CNNs With Normalized Kernels. AAAI[Internet]. 2018[cited 2023]; .


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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