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

Conquering the CNN Over-Parameterization Dilemma: A Volterra Filtering Approach for Action Recognition

February 1, 2023

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Authors

Siddharth Roheda

North Carolina State University at Raleigh


Hamid Krim

North Carolina State University at Raleigh


DOI:

10.1609/aaai.v34i07.6870


Abstract:

The importance of inference in Machine Learning (ML) has led to an explosive number of different proposals in ML, and particularly in Deep Learning. In an attempt to reduce the complexity of Convolutional Neural Networks, we propose a Volterra filter-inspired Network architecture. This architecture introduces controlled non-linearities in the form of interactions between the delayed input samples of data. We propose a cascaded implementation of Volterra Filtering so as to significantly reduce the number of parameters required to carry out the same classification task as that of a conventional Neural Network. We demonstrate an efficient parallel implementation of this Volterra Neural Network (VNN), along with its remarkable performance while retaining a relatively simpler and potentially more tractable structure. Furthermore, we show a rather sophisticated adaptation of this network to nonlinearly fuse the RGB (spatial) information and the Optical Flow (temporal) information of a video sequence for action recognition. The proposed approach is evaluated on UCF-101 and HMDB-51 datasets for action recognition, and is shown to outperform state of the art CNN approaches.

Topics: AAAI

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Siddharth Roheda||Hamid Krim Conquering the CNN Over-Parameterization Dilemma: A Volterra Filtering Approach for Action Recognition Proceedings of the AAAI Conference on Artificial Intelligence (2020) 11948-11956.

Siddharth Roheda||Hamid Krim Conquering the CNN Over-Parameterization Dilemma: A Volterra Filtering Approach for Action Recognition AAAI 2020, 11948-11956.

Siddharth Roheda||Hamid Krim (2020). Conquering the CNN Over-Parameterization Dilemma: A Volterra Filtering Approach for Action Recognition. Proceedings of the AAAI Conference on Artificial Intelligence, 11948-11956.

Siddharth Roheda||Hamid Krim. Conquering the CNN Over-Parameterization Dilemma: A Volterra Filtering Approach for Action Recognition. Proceedings of the AAAI Conference on Artificial Intelligence 2020 p.11948-11956.

Siddharth Roheda||Hamid Krim. 2020. Conquering the CNN Over-Parameterization Dilemma: A Volterra Filtering Approach for Action Recognition. "Proceedings of the AAAI Conference on Artificial Intelligence". 11948-11956.

Siddharth Roheda||Hamid Krim. (2020) "Conquering the CNN Over-Parameterization Dilemma: A Volterra Filtering Approach for Action Recognition", Proceedings of the AAAI Conference on Artificial Intelligence, p.11948-11956

Siddharth Roheda||Hamid Krim, "Conquering the CNN Over-Parameterization Dilemma: A Volterra Filtering Approach for Action Recognition", AAAI, p.11948-11956, 2020.

Siddharth Roheda||Hamid Krim. "Conquering the CNN Over-Parameterization Dilemma: A Volterra Filtering Approach for Action Recognition". Proceedings of the AAAI Conference on Artificial Intelligence, 2020, p.11948-11956.

Siddharth Roheda||Hamid Krim. "Conquering the CNN Over-Parameterization Dilemma: A Volterra Filtering Approach for Action Recognition". Proceedings of the AAAI Conference on Artificial Intelligence, (2020): 11948-11956.

Siddharth Roheda||Hamid Krim. Conquering the CNN Over-Parameterization Dilemma: A Volterra Filtering Approach for Action Recognition. AAAI[Internet]. 2020[cited 2023]; 11948-11956.


ISSN: 2374-3468


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