Deep Rotating Kernel Convolution Neural Network
Crino Shin, Jong-Pil Yun · 2019 Third IEEE International Conference on Robotic Computing (IRC) · 2019
This paper describes a method that can be efficiently applied to data with rotational invariant characteristics such as texture. We propose a simple and highly scalable model that has excellent rotational invariant characteristics by using Rotating Kernel Convolution (RK Conv) which convolves and rotates kernel and Global Average Pooling (GAP) which invariant features to absolute position. The proposed model shows the state of the art performance in experiments under the same conditions as those in previous papers.