# Exploring Feature Reuse in DenseNet Architectures

Source: [Exploring Feature Reuse in DenseNet Architectures](<https://www.sota2.com/research/paper/68fa64671e209131cea046fb>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:03:11.991Z

Authors: Andy Hess

Published: 2018-06-05T21:11:23.000Z

## Abstract

Densely Connected Convolutional Networks (DenseNets) have been shown to achieve state-of-the-art results on image classification tasks while using fewer parameters and computation than competing methods. Since each layer in this architecture has full access to the feature maps of all previous layers, the network is freed from the burden of having to relearn previously useful features, thus alleviating issues with vanishing gradients. In this work we explore the question: To what extent is it necessary to connect to all previous layers in order to reap the benefits of feature reuse? To this end, we introduce the notion of local dense connectivity and present evidence that less connectivity, allowing for increased growth rate at a fixed network capacity, can achieve a more efficient reuse of features and lead to higher accuracy in dense architectures.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1806.01935>)

[PDF](<https://arxiv.org/pdf/1806.01935.pdf>)