Conditional Synthetic Food Image Generation
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ID: 282991
2023
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Abstract
Generative Adversarial Networks (GAN) have been widely investigated for image
synthesis based on their powerful representation learning ability. In this
work, we explore the StyleGAN and its application of synthetic food image
generation. Despite the impressive performance of GAN for natural image
generation, food images suffer from high intra-class diversity and inter-class
similarity, resulting in overfitting and visual artifacts for synthetic images.
Therefore, we aim to explore the capability and improve the performance of GAN
methods for food image generation. Specifically, we first choose StyleGAN3 as
the baseline method to generate synthetic food images and analyze the
performance. Then, we identify two issues that can cause performance
degradation on food images during the training phase: (1) inter-class feature
entanglement during multi-food classes training and (2) loss of high-resolution
detail during image downsampling. To address both issues, we propose to train
one food category at a time to avoid feature entanglement and leverage image
patches cropped from high-resolution datasets to retain fine details. We
evaluate our method on the Food-101 dataset and show improved quality of
generated synthetic food images compared with the baseline. Finally, we
demonstrate the great potential of improving the performance of downstream
tasks, such as food image classification by including high-quality synthetic
training samples in the data augmentation.
| Reference Key |
zhu2023conditional
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|---|---|
| Authors | Wenjin Fu; Yue Han; Jiangpeng He; Sriram Baireddy; Mridul Gupta; Fengqing Zhu |
| Journal | arXiv |
| Year | 2023 |
| DOI |
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