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Paper Abstract and Keywords
Presentation 2024-03-12 14:45
Investigating the Effect of Skip Connection on Learning Dynamics in the Initial Learning Process of Deep Neural Networks
Ryodo Yuge, Takashi Shinozaki (Kindai Univ.) NC2023-59
Abstract (in Japanese) (See Japanese page) 
(in English) We visualize the impact of skip connections, a key element in residual networks (ResNet), and visualize its impact on the learning dynamics. As its name suggests, the high performance of ResNet is achieved by its residuals, in which input signals are directly added to output signals without processing by the layer, through a branching structure called skip connections. Feedforward neural networks, by their nature, require that all layers prior to the layer to be trained have already been trained. This is because if there is even one untrained layer in the propagation, the propagating signal will be turned into random noise by the random weight. Therefore, deep neural networks are likely to learn from the layer closest to the input, where the training signal is hardest to reach, resulting in difficulty in learning with more than several dozen layers. The skip connection circumvents this limitation by bypassing the untrained layers, allowing learning in extremely many layers, and becoming the fundamental structure of high-performance deep neural networks. The skip connection is also used in Transformer, the basic structure of large-scale language models (LLMs), the fountainhead of recent AI breakthroughs, and is arguably one of the greatest inventions in deep learning. On the other hand, the effects of skip connections on learning dynamics have not been fully investigated, and clarifying their effects would be important for a true understanding of the mechanisms of deep learning and for further performance improvement. Therefore, this study visualized the learning process of ResNet, layer by layer, and identified the effect of skip connections on the learning dynamics.
Keyword (in Japanese) (See Japanese page) 
(in English) Skip Connection / Neural Networks / Deep Learning / Dynamics / Visualization / / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 418, NC2023-59, pp. 94-94, March 2024.
Paper # NC2023-59 
Date of Issue 2024-03-04 (NC) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF NC2023-59

Conference Information
Committee NC MBE  
Conference Date 2024-03-11 - 2024-03-12 
Place (in Japanese) (See Japanese page) 
Place (in English) The Univ. of Tokyo 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Brain architecture, General 
Paper Information
Registration To NC 
Conference Code 2024-03-NC-MBE 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Investigating the Effect of Skip Connection on Learning Dynamics in the Initial Learning Process of Deep Neural Networks 
Sub Title (in English)  
Keyword(1) Skip Connection  
Keyword(2) Neural Networks  
Keyword(3) Deep Learning  
Keyword(4) Dynamics  
Keyword(5) Visualization  
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Keyword(8)  
1st Author's Name Ryodo Yuge  
1st Author's Affiliation Kindai University (Kindai Univ.)
2nd Author's Name Takashi Shinozaki  
2nd Author's Affiliation Kindai University (Kindai Univ.)
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Speaker Author-2 
Date Time 2024-03-12 14:45:00 
Presentation Time 25 minutes 
Registration for NC 
Paper # NC2023-59 
Volume (vol) vol.123 
Number (no) no.418 
Page p.94 
#Pages 1 
Date of Issue 2024-03-04 (NC) 

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