# Using accumulation to optimize deep residual neural nets

Source: [Using accumulation to optimize deep residual neural nets](<https://www.sota2.com/research/paper/68fa64241e209131cea02cbb>)

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

Data updated: 2025-10-25T20:57:11.341Z

Authors: Yatin Saraiya

Published: 2018-01-14T07:17:00.000Z

## Abstract

Residual Neural Networks \[1\] won first place in all five main tracks of the ImageNet and COCO 2015 competitions. This kind of network involves the creation of pluggable modules such that the output contains a residual from the input. The residual in that paper is the identity function. We propose to include residuals from all lower layers, suitably normalized, to create the residual. This way, all previous layers contribute equally to the output of a layer. We show that our approach is an improvement on \[1\] for the CIFAR-10 dataset.

## 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/1803.05778>)

[PDF](<https://arxiv.org/pdf/1803.05778.pdf>)