A general approach to bridge the reality-gap
Michael Lomnitz, Zigfried Hampel-Arias, Nina Lopatina, Felipe A. Mejia

TL;DR
This paper introduces an unsupervised deep learning transformation that aligns arbitrary images with a canonical distribution, improving the transferability of models to real-world data without extensive real-world training data.
Contribution
It proposes a general, unsupervised transformation method to bridge the reality gap, reducing reliance on domain-specific adaptation and transfer learning.
Findings
Recovered half of the performance loss on distorted datasets
Effective on real-world images captured under varying lighting conditions
Applicable to pre-trained ImageNet models
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…
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Taxonomy
TopicsDomain Adaptation and Few-Shot Learning · Multimodal Machine Learning Applications · Advanced Neural Network Applications
