# Using mixup as regularization and tuning hyper-parameters for ResNets

Source: [Using mixup as regularization and tuning hyper-parameters for ResNets](<https://www.sota2.com/research/paper/68fa6b461e209131cea2d321>)

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

Data updated: 2025-10-25T23:44:09.187Z

Authors: Venkata Bhanu Teja Pallakonda

Published: 2021-11-23T02:33:24.000Z

## Abstract

While novel computer vision architectures are gaining traction, the impact of model architectures is often related to changes or exploring in training methods. Identity mapping-based architectures ResNets and DenseNets have promised path-breaking results in the image classification task and are go-to methods for even now if the data given is fairly limited. Considering the ease of training with limited resources this work revisits the ResNets and improves the ResNet50 \\cite{resnets} by using mixup data-augmentation as regularization and tuning the hyper-parameters.

## 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/2111.11616>)

[PDF](<https://arxiv.org/pdf/2111.11616.pdf>)