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Title: Generative adversarial network (GAN) for image synthesis
Authors: Hou, Boyu
Keywords: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Hou, B. (2022). Generative adversarial network (GAN) for image synthesis. Final Year Project (FYP), Nanyang Technological University, Singapore.
Project: A3277-211
Abstract: 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.
Schools: School of Electrical and Electronic Engineering 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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