Data Augmentation via Mixed Class Interpolation using Cycle-Consistent Generative Adversarial Networks Applied to Cross-Domain Imagery

• URL: http://arxiv.org/abs/2005.02436v2
• Date: Fri, 1 Jan 2021 22:29:13 GMT
• Title: Data Augmentation via Mixed Class Interpolation using Cycle-Consistent Generative Adversarial Networks Applied to Cross-Domain Imagery
• Authors: Hiroshi Sasaki, Chris G. Willcocks, Toby P. Breckon
• Abstract summary: Machine learning driven object detection and classification within non-visible imagery has an important role in many fields. However, such applications often suffer due to the limited quantity and variety of non-visible spectral domain imagery. This paper proposes and evaluates a novel data augmentation approach that leverages the more readily available visible-band imagery.
• Score: 16.870604081967866
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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).

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