# ChoiceNet: CNN learning through choice of multiple feature map representations

## TL;DR

ChoiceNet is a novel CNN architecture with skip connections and feature reuse that improves gradient flow, reduces parameters, and performs well on object recognition and segmentation benchmarks.

## Contribution

It introduces a highly connected CNN architecture with channelwise concatenations to enhance feature reuse and training efficiency.

## Key findings

- Reduces vanishing gradient problems
- Achieves competitive performance on ImageNet, CIFAR, SVHN, and CamVid
- Uses fewer parameters without sacrificing accuracy

## Abstract

We introduce a new architecture called ChoiceNet where each layer of the network is highly connected with skip connections and channelwise concatenations. This enables the network to alleviate the problem of vanishing gradients, reduces the number of parameters without sacrificing performance, and encourages feature reuse. We evaluate our proposed architecture on three benchmark datasets for object recognition tasks (ImageNet, CIFAR- 10, CIFAR-100, SVHN) and on a semantic segmentation dataset (CamVid).