In this review article series, we will focus on a plethora of GANs for computer vision applications. Specifically, we will slowly build upon the ideas and the principles that led to the evolution of generative adversarial networks (GAN). We will encounter different tasks such as conditional image generation, 3D object generation, video synthesis.
Contents
2. Vanilla GAN (Generative Adversarial Networks)
3. Conditional GAN (Conditional Generative Adversarial Nets)
4. DCGAN (Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks)
5. Info GAN: Representation Learning by Information Maximizing Generative Adversarial Nets
6. Improved Techniques for Training GANs
第一部分：https://theaisummer.com/gan-computer-vision/
The previous post was more or less introductory in GANs, generative learning, and computer vision. We reached the point of generating distinguishable image features in 128x128 images. In this part, we will continue our GAN journey in computer vision diving in more complex designs and better visual results. We will see mode collapse, 3D object generation, single RGB image to 3D object generation, and improved quality image to image mappings.
Contents
1. AC-GAN (Conditional Image Synthesis with Auxiliary Classifier GANs)
3. PacGAN (The power of two samples in generative adversarial networks)
4. Pix2Pix GAN (Image-to-Image Translation with Conditional Adversarial Networks)
5. Cycle-GAN (Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks)
第二部分：https://theaisummer.com/gan-computer-vision-object-generation/