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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 35 / No. 9: AAAI-21 Technical Tracks 9

Interpretable Embedding Procedure Knowledge Transfer via Stacked Principal Component Analysis and Graph Neural Network

February 1, 2023

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Authors

Seunghyun Lee

Inha University


Byung Cheol Song

Inha University


DOI:

10.1609/aaai.v35i9.17009


Abstract:

Knowledge distillation (KD) is one of the most useful techniques for light-weight neural networks. Although neural networks have a clear purpose of embedding datasets into the low-dimensional space, the existing knowledge was quite far from this purpose and provided only limited information. We argue that good knowledge should be able to interpret the embedding procedure. This paper proposes a method of generating interpretable embedding procedure (IEP) knowledge based on principal component analysis, and distilling it based on a message passing neural network. Experimental results show that the student network trained by the proposed KD method improves 2.28% in the CIFAR100 dataset, which is a higher performance than the state-of-the-art (SOTA) method. We also demonstrate that the embedding procedure knowledge is interpretable via visualization of the proposed KD process. The implemented code is available at https://github.com/sseung0703/IEPKT.

Topics: AAAI

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HOW TO CITE:

Seunghyun Lee||Byung Cheol Song Interpretable Embedding Procedure Knowledge Transfer via Stacked Principal Component Analysis and Graph Neural Network Proceedings of the AAAI Conference on Artificial Intelligence (2021) 8297-8305.

Seunghyun Lee||Byung Cheol Song Interpretable Embedding Procedure Knowledge Transfer via Stacked Principal Component Analysis and Graph Neural Network AAAI 2021, 8297-8305.

Seunghyun Lee||Byung Cheol Song (2021). Interpretable Embedding Procedure Knowledge Transfer via Stacked Principal Component Analysis and Graph Neural Network. Proceedings of the AAAI Conference on Artificial Intelligence, 8297-8305.

Seunghyun Lee||Byung Cheol Song. Interpretable Embedding Procedure Knowledge Transfer via Stacked Principal Component Analysis and Graph Neural Network. Proceedings of the AAAI Conference on Artificial Intelligence 2021 p.8297-8305.

Seunghyun Lee||Byung Cheol Song. 2021. Interpretable Embedding Procedure Knowledge Transfer via Stacked Principal Component Analysis and Graph Neural Network. "Proceedings of the AAAI Conference on Artificial Intelligence". 8297-8305.

Seunghyun Lee||Byung Cheol Song. (2021) "Interpretable Embedding Procedure Knowledge Transfer via Stacked Principal Component Analysis and Graph Neural Network", Proceedings of the AAAI Conference on Artificial Intelligence, p.8297-8305

Seunghyun Lee||Byung Cheol Song, "Interpretable Embedding Procedure Knowledge Transfer via Stacked Principal Component Analysis and Graph Neural Network", AAAI, p.8297-8305, 2021.

Seunghyun Lee||Byung Cheol Song. "Interpretable Embedding Procedure Knowledge Transfer via Stacked Principal Component Analysis and Graph Neural Network". Proceedings of the AAAI Conference on Artificial Intelligence, 2021, p.8297-8305.

Seunghyun Lee||Byung Cheol Song. "Interpretable Embedding Procedure Knowledge Transfer via Stacked Principal Component Analysis and Graph Neural Network". Proceedings of the AAAI Conference on Artificial Intelligence, (2021): 8297-8305.

Seunghyun Lee||Byung Cheol Song. Interpretable Embedding Procedure Knowledge Transfer via Stacked Principal Component Analysis and Graph Neural Network. AAAI[Internet]. 2021[cited 2023]; 8297-8305.


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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