# A general approach to bridge the reality-gap

Source: [A general approach to bridge the reality-gap](<https://www.sota2.com/research/paper/68fa68451e209131cea1aee6>)

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

Data updated: 2025-10-25T22:32:00.581Z

Authors: Michael Lomnitz, Zigfried Hampel-Arias, Nina Lopatina, Felipe A. Mejia

Published: 2020-09-03T18:19:28.000Z

## Abstract

Employing machine learning models in the real world requires collecting large amounts of data, which is both time consuming and costly to collect. A common approach to circumvent this is to leverage existing, similar data-sets with large amounts of labelled data. However, models trained on these canonical distributions do not readily transfer to real-world ones. Domain adaptation and transfer learning are often used to breach this "reality gap", though both require a substantial amount of real-world data. In this paper we discuss a more general approach: we propose learning a general transformation to bring arbitrary images towards a canonical distribution where we can naively apply the trained machine learning models. This transformation is trained in an unsupervised regime, leveraging data augmentation to generate off-canonical examples of images and training a Deep Learning model to recover their original counterpart. We quantify the performance of this transformation using pre-trained ImageNet classifiers, demonstrating that this procedure can recover half of the loss in performance on the distorted data-set. We then validate the effectiveness of this approach on a series of pre-trained ImageNet models on a real world data set collected by printing and photographing images in different lighting conditions.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2009.01865>)

[PDF](<https://arxiv.org/pdf/2009.01865.pdf>)