A general approach to bridge the reality-gap

• URL: http://arxiv.org/abs/2009.01865v1
• Date: Thu, 3 Sep 2020 18:19:28 GMT
• Title: A general approach to bridge the reality-gap
• Authors: Michael Lomnitz, Zigfried Hampel-Arias, Nina Lopatina, Felipe A. Mejia
• Abstract summary: A common approach to circumvent this is to leverage existing, similar data-sets with large amounts of labelled data. We propose learning a general transformation to bring arbitrary images towards a canonical distribution. This transformation is trained in an unsupervised regime, leveraging data augmentation to generate off-canonical examples of images.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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