# Tandem Blocks in Deep Convolutional Neural Networks

Source: [Tandem Blocks in Deep Convolutional Neural Networks](<https://www.sota2.com/research/paper/68fa646c1e209131cea049f3>)

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

Data updated: 2025-10-25T21:04:03.574Z

Authors: Chris Hettinger, Tanner Christensen, Jeffrey Humpherys, Tyler J. Jarvis

Published: 2018-06-01T00:19:05.000Z

## Abstract

Due to the success of residual networks (resnets) and related architectures, shortcut connections have quickly become standard tools for building convolutional neural networks. The explanations in the literature for the apparent effectiveness of shortcuts are varied and often contradictory. We hypothesize that shortcuts work primarily because they act as linear counterparts to nonlinear layers. We test this hypothesis by using several variations on the standard residual block, with different types of linear connections, to build small image classification networks. Our experiments show that other kinds of linear connections can be even more effective than the identity shortcuts. Our results also suggest that the best type of linear connection for a given application may depend on both network width and depth.

## 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/1806.00145>)

[PDF](<https://arxiv.org/pdf/1806.00145.pdf>)