Gradually Updated Neural Networks for Large-Scale Image Recognition

Understand

Depth is one of the keys that make neural networks succeed in the task of large-scale image recognition.

• The state-of-the-art network architectures usually increase the depths by cascading convolutional layers or building blocks.
• In this paper, we present an alternative method to increase the depth.
• Our method is by introducing computation orderings to the channels within convolutional layers or blocks, based on which we gradually compute the outputs in a channel-wise manner.