CNN to Capsule Network Transformation

Abstract

Capsule Network has been recently proposed which outperforms CNN in specific tasks. Due to the network architecture differences between Capsule Network and CNN, Capsule Network could not use transfer learning which is very frequently used in CNN. In this paper, we propose a transfer learning method which can easily transfer CNN to Capsule Network. We achieved by stacking pre-trained CNN and used the proposed capsule random transformer to interact individual CNN each other which will form a Capsule Network. We applied this method to U-net and achieved to create a capsule based method that has similar accuracy compared to U-net. We show the results on cell segmentation dataset. Our capsule network successfully archives higher accuracy compared to other Capsule Network based semantic segmentation methods.