Abstract:
"
Semantic image segmentation is a crucial task in various fields that use computer-vision based
applications. Generative Adversarial Networks (GANs) are attracting widespread interest in
the data science community for their prowess in image feature recognition due to their
adversarial nature of training. Neural Architecture Search (NAS) is known as the process of
obtaining a neural architectural schema that performs the best for a particular task. NAS has
been applied in GANs, and it achieved striking success compared to human-designed
architectures in conditional and unconditional image generation and GAN-compression. The
research was inspired by the success of NAS applied in GANs.
This research project proposes a novel framework for NAS in GANs for semantic image
segmentation called iSegmentor. After extensive research on related works, the architecture of
the Pix2Pix GAN variant was selected for the proposed approach. The architecture of the
Pix2Pix GAN consists of a U-Net as the Generator and a patchGAN classifier as the
Discriminator. The NAS component is searched for U-Net architectures using PASCAL VOC
2012 dataset. The NAS component is adapted from using the NAS-Unet research proposed by
Weng et al. in 2019. The NAS searched architecture was used as the Generator of the proposed
GAN by transferring the searched architecture from the PASCAL VOC 2012 dataset to the
Cityscapes dataset. To determine the success of the proposed approach, quantitative analysis
was performed with Mean Pixel Accuracy (MPA) and mean Intersection over Union (mIoU)
metrics. Several experiments were done on the Cityscape validation set and achieved 81.73
MPA and 71.91 mIoU. Generalisation of the proposed approach was tested with CamVid
dataset and achieved 80.41 MPA and 70.63 mIoU. The proposed approach outperformed
several NAS in semantic segmentation approaches and GANs in semantic segmentation
approaches. This study is a preliminary attempt to apply NAS for semantic segmentation using
GANs. Further, this research has raised many possible areas in need of further investigation.
"
