论文标题
通过使用周期一致的生成对抗网络，通过混合类插值进行数据增强
Data Augmentation via Mixed Class Interpolation using Cycle-Consistent Generative Adversarial Networks Applied to Cross-Domain Imagery
论文作者
论文摘要
机器学习驱动的对象检测和非可见图像中的分类在许多领域中都具有重要作用，例如夜视，全天候监视和航空安全。但是，与可见频段图像的高数据可用性相比，这种应用通常由于数量有限和多种不可访问的光谱域图像而遭受损失，这很容易使当代深度学习驱动的检测和分类方法。为了解决这个问题，本文提出并评估了一种新型的数据增强方法，该方法通过生成域传输模型利用了更容易获得的可见波段图像。该模型可以通过图像到图像（I2i）的翻译从可见图像域合成大量不可访问的域图像。此外，我们表明，通过我们的新型有条件的自行车混合增强（C2GMA）方法，插值混合类（不可访问的域）图像示例的产生可以导致非可见的域分类任务的质量显着提高，这些任务否则由于数据可用性有限，否则否则却遭受了损失。在合成孔径雷达（SAR）结构域中的分类中，我们的方法是根据Statoil/C-Core冰山分类器挑战数据集的变化进行评估的，并且达到75.4％的准确性，表明与传统数据增强策略（旋转，混合和混合和混合物）相比，相比之下表明有了显着改善。
Machine learning driven object detection and classification within non-visible imagery has an important role in many fields such as night vision, all-weather surveillance and aviation security. However, such applications often suffer due to the limited quantity and variety of non-visible spectral domain imagery, in contrast to the high data availability of visible-band imagery that readily enables contemporary deep learning driven detection and classification approaches. To address this problem, this paper proposes and evaluates a novel data augmentation approach that leverages the more readily available visible-band imagery via a generative domain transfer model. The model can synthesise large volumes of non-visible domain imagery by image-to-image (I2I) translation from the visible image domain. Furthermore, we show that the generation of interpolated mixed class (non-visible domain) image examples via our novel Conditional CycleGAN Mixup Augmentation (C2GMA) methodology can lead to a significant improvement in the quality of non-visible domain classification tasks that otherwise suffer due to limited data availability. Focusing on classification within the Synthetic Aperture Radar (SAR) domain, our approach is evaluated on a variation of the Statoil/C-CORE Iceberg Classifier Challenge dataset and achieves 75.4% accuracy, demonstrating a significant improvement when compared against traditional data augmentation strategies (Rotation, Mixup, and MixCycleGAN).
