Generative adversarial network (GAN) for image synthesis

• 主要作者: Hou, Boyu
• 其他作者: Wen Bihan
• 格式: Final Year Project
• 語言: English
• 出版: Nanyang Technological University 2022
• 主題: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence; Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
• 在線閱讀: https://hdl.handle.net/10356/158045

Recently, Conditional generative adversarial network (cGAN) plays an important role in image synthesis tasks and Vision Transformer (ViT) with self-attention mechanism have shown SOTA performance on computer vision field. In this report, I extent ViT to image synthesis tasks. I propose two ViT-based generator architectures with upsampling and transposed convolution encoders and one ViT-based discriminator. I demonstrate that my models, named cViTGAN, are capable of image synthesis task. I perform experiments on six different benchmarks, the models achieve comparable performance to the baseline models. My work shows that we can achieve reasonable results with ViT-based models.