水下作业中的迁移学习

M. Skaldebø, Albert Sans Muntadas, I. Schjølberg
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引用次数: 3

摘要

本文研究了一种减少在水下环境中基于视觉的操作训练中应用模拟数据时出现的现实差距的方法。仿真领域与真实领域的知识差异称为现实差距。所提出的工作的目的是适应和测试一种方法,将在模拟环境中获得的知识转移到真实环境中。重点关注的主要方法是机器学习框架CycleGAN,映射所需的特征以重建环境。总体目标是使在模拟环境中训练的框架能够在应用于现实世界时识别所需的特征。学习迁移的性能是通过从新的测试数据重新创建不同环境的能力来衡量的。结果表明,CycleGAN框架能够映射未标记数据集呈现的水下环境特征。评估指标,如平均精度(AP)或FCN-score可用于进一步评估结果。此外,这需要标记数据,这为当前数据集提供了额外的开发。
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Transfer Learning in Underwater Operations
This paper investigates a method for reducing the reality gap that occurs when applying simulated data in training for vision-based operations in a subsea environment. The distinction in knowledge in the simulated and real domains is denoted the reality gap. The objective of the presented work is to adapt and test a method for transferring knowledge obtained in a simulated environment into the real environment. The main method in focus is the machine learning framework CycleGAN, mapping desired features in order to recreate environments. The overall goal is to enable a framework trained in a simulated environment to recognize the desired features when applied in the real world. The performance of the learning transfer is measured by the ability to recreate the different environments from new test data. The obtained results demonstrates that the CycleGAN framework is able to map features characteristic for an underwater environment presented with the unlabeled datasets. Evaluation metrics, such as Average precision (AP) or FCN-score can be used to further evaluate the results. Moreover, this requires labeled data, which provides additional development of the current datasets.
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