Distribution Padding in Convolutional Neural Networks
| Autor: | Anh-Duc Nguyen, Sewoong Ahn, Sanghoon Lee, Seonghwa Choi, Jinwoo Kim, Woojae Kim |
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| Rok vydání: | 2019 |
| Předmět: |
Distribution (mathematics)
Computer science Histogram Transfer (computing) 0202 electrical engineering electronic engineering information engineering 020206 networking & telecommunications 020201 artificial intelligence & image processing 02 engineering and technology Algorithm Convolutional neural network Padding Convolution |
| Zdroj: | ICIP |
| Popis: | Even though zero padding is usually a staple in convolutional neural networks to maintain the output size, it is highly suspicious because it significantly alters the input distribution around border region. To mitigate this problem, in this paper, we propose a new padding technique termed as distribution padding. The goal of the method is to approximately maintain the statistics of the input border regions. We introduce two different ways to achieve our goal. In both approaches, the padded values are derived from the means of the border patches, but those values are handled in a different way in each variant. Through extensive experiments on image classification and style transfer using different architectures, we demonstrate that the proposed padding technique consistently outperforms the default zero padding, and hence can be a potential candidate for its replacement. |
| Databáze: | OpenAIRE |
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