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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence / EAAI-20

Regularizing Fully Convolutional Networks for Time Series Classification by Decorrelating Filters

February 1, 2023

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Authors

Kaushal Paneri

Northeastern University


Vishnu TV

TCS Research


Pankaj Malhotra

TCS Research


Lovekesh Vig

TCS Research


Gautam Shroff

TCS Research


DOI:

10.1609/aaai.v33i01.330110003


Abstract:

Deep neural networks are prone to overfitting, especially in small training data regimes. Often, these networks are overparameterized and the resulting learned weights tend to have strong correlations. However, convolutional networks in general, and fully convolution neural networks (FCNs) in particular, have been shown to be relatively parameter efficient, and have recently been successfully applied to time series classification tasks. In this paper, we investigate the application of different regularizers on the correlation between the learned convolutional filters in FCNs using Batch Normalization (BN) as a regularizer for time series classification (TSC) tasks. Results demonstrate that despite orthogonal initialization of the filters, the average correlation across filters (especially for filters in higher layers) tends to increase as training proceeds, indicating redundancy of filters. To mitigate this redundancy, we propose a strong regularizer, using simple yet effective filter decorrelation. Our proposed method yields significant gains in classification accuracy for 44 diverse time series datasets from the UCR TSC benchmark repository.

Topics: AAAI

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Kaushal Paneri||Vishnu TV||Pankaj Malhotra||Lovekesh Vig||Gautam Shroff Regularizing Fully Convolutional Networks for Time Series Classification by Decorrelating Filters Proceedings of the AAAI Conference on Artificial Intelligence (2019) 10003-10004.

Kaushal Paneri||Vishnu TV||Pankaj Malhotra||Lovekesh Vig||Gautam Shroff Regularizing Fully Convolutional Networks for Time Series Classification by Decorrelating Filters AAAI 2019, 10003-10004.

Kaushal Paneri||Vishnu TV||Pankaj Malhotra||Lovekesh Vig||Gautam Shroff (2019). Regularizing Fully Convolutional Networks for Time Series Classification by Decorrelating Filters. Proceedings of the AAAI Conference on Artificial Intelligence, 10003-10004.

Kaushal Paneri||Vishnu TV||Pankaj Malhotra||Lovekesh Vig||Gautam Shroff. Regularizing Fully Convolutional Networks for Time Series Classification by Decorrelating Filters. Proceedings of the AAAI Conference on Artificial Intelligence 2019 p.10003-10004.

Kaushal Paneri||Vishnu TV||Pankaj Malhotra||Lovekesh Vig||Gautam Shroff. 2019. Regularizing Fully Convolutional Networks for Time Series Classification by Decorrelating Filters. "Proceedings of the AAAI Conference on Artificial Intelligence". 10003-10004.

Kaushal Paneri||Vishnu TV||Pankaj Malhotra||Lovekesh Vig||Gautam Shroff. (2019) "Regularizing Fully Convolutional Networks for Time Series Classification by Decorrelating Filters", Proceedings of the AAAI Conference on Artificial Intelligence, p.10003-10004

Kaushal Paneri||Vishnu TV||Pankaj Malhotra||Lovekesh Vig||Gautam Shroff, "Regularizing Fully Convolutional Networks for Time Series Classification by Decorrelating Filters", AAAI, p.10003-10004, 2019.

Kaushal Paneri||Vishnu TV||Pankaj Malhotra||Lovekesh Vig||Gautam Shroff. "Regularizing Fully Convolutional Networks for Time Series Classification by Decorrelating Filters". Proceedings of the AAAI Conference on Artificial Intelligence, 2019, p.10003-10004.

Kaushal Paneri||Vishnu TV||Pankaj Malhotra||Lovekesh Vig||Gautam Shroff. "Regularizing Fully Convolutional Networks for Time Series Classification by Decorrelating Filters". Proceedings of the AAAI Conference on Artificial Intelligence, (2019): 10003-10004.

Kaushal Paneri||Vishnu TV||Pankaj Malhotra||Lovekesh Vig||Gautam Shroff. Regularizing Fully Convolutional Networks for Time Series Classification by Decorrelating Filters. AAAI[Internet]. 2019[cited 2023]; 10003-10004.


ISSN: 2374-3468


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