Title | ||
---|---|---|
Image classification in frequency domain with 2SReLU: A second harmonics superposition activation function |
Abstract | ||
---|---|---|
Deep Convolutional Neural Networks are able to identify complex patterns and perform tasks with super-human capabilities. However, besides the exceptional results, they are not completely understood and it is still impractical to hand-engineer similar solutions. In this work, an image classification Convolutional Neural Network and its building blocks are described from a frequency domain perspective. Some network layers have established counterparts in the frequency domain like the convolutional and pooling layers. We propose the 2SReLU layer, a novel non-linear activation function that preserves high frequency components in deep networks. A convolution-free network is presented, and it is demonstrated that in the frequency domain it is possible to achieve competitive results without using the computationally costly convolution operation. A source code implementation in PyTorch is provided at: https://gitlab.com/thomio/2srelu. (C) 2021 Elsevier B.V. All rights reserved. |
Year | DOI | Venue |
---|---|---|
2021 | 10.1016/j.asoc.2021.107851 | APPLIED SOFT COMPUTING |
Keywords | DocType | Volume |
Image classification, Artificial neural networks, Activation function, Frequency domain | Journal | 112 |
ISSN | Citations | PageRank |
1568-4946 | 0 | 0.34 |
References | Authors | |
0 | 2 |
Name | Order | Citations | PageRank |
---|---|---|---|
Thomio Watanabe | 1 | 0 | 0.34 |
Denis Fernando Wolf | 2 | 47 | 9.86 |