Data Augmentation via Mixed Class Interpolation using Cycle-Consistent Generative Adversarial Networks Applied to Cross-Domain Imagery
Sasaki, H.; Willcocks, C.G.; Breckon, T.P.
Dr Chris Willcocks email@example.com
Professor Toby Breckon firstname.lastname@example.org
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).
Sasaki, H., Willcocks, C., & Breckon, T. (2021). Data Augmentation via Mixed Class Interpolation using Cycle-Consistent Generative Adversarial Networks Applied to Cross-Domain Imagery. . https://doi.org/10.1109/icpr48806.2021.9413023
|Conference Name||25th International Conference on Pattern Recognition (ICPR 2020)|
|Conference Location||Milan, Italy|
|Start Date||Jan 10, 2021|
|End Date||Jan 15, 2021|
|Acceptance Date||Oct 11, 2020|
|Online Publication Date||May 5, 2021|
|Deposit Date||Oct 25, 2020|
|Publicly Available Date||Oct 27, 2020|
Accepted Conference Proceeding
© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
You might also like
Exact-NeRF: An Exploration of a Precise Volumetric Parameterization for Neural Radiance Fields
Dynamic Unary Convolution in Transformers
Denoising Diffusion Probabilistic Models on SO(3) for Rotational Alignment
MedNeRF: Medical Neural Radiance Fields for Reconstructing 3D-aware CT-Projections from a Single X-ray