Log-Polar Space Convolution for Convolutional Neural Networks

Understand

Convolutional neural networks use regular quadrilateral convolution kernels to extract features.

• Since the number of parameters increases quadratically with the size of the convolution kernel, many popular models use small convolution kernels, resulting in small local receptive fields in lower layers.
• This paper proposes a novel log-polar space convolution (LPSC) method, where the convolution kernel is elliptical and adaptively divides its local receptive field into different regions according to the relative directions and logarithmic distances.
• The local receptive field grows exponentially with the number of distance levels.