Generative adversarial network (GAN) for image synthesis

Generative adversarial network (GAN) for image synthesis

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...

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Bibliographic Details
Main Author: Hou, Boyu
Other Authors: Wen Bihan
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2022
Subjects:
Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
Online Access:https://hdl.handle.net/10356/158045
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Institution: Nanyang Technological University
Language: English
Description
Summary: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.

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