Scale-Invariant Convolutional Neural Networks

Understand

Even though convolutional neural networks (CNN) has achieved near-human performance in various computer vision tasks, its ability to tolerate scale variations is limited.

• The popular practise is making the model bigger first, and then train it with data augmentation using extensive scale-jittering.
• In this paper, we propose a scaleinvariant convolutional neural network (SiCNN), a modeldesigned to incorporate multi-scale feature exaction and classification into the network structure.
• SiCNN uses a multi-column architecture, with each column focusing on a particular scale.