GANs in computer vision ebook
In this article-series we are reviewing the most fundamental works of Generative Adversarial Networks in Computer Vision. We start from the very beginning from concepts such as generative learning, adversarial learning. We provide some code and illustrations for educational purposes. The goal is to focus on the intuition of the models, by tackling the multiple problems that arise when training a GAN. We have thoroughly analyzed more than 20 papers in 6 different articles in a chronological order. We will continue to update the GAN series, based on the newer publications or older ones that we skipped. We do hope that this series will provide you a big overview of the field, so that you will not need to read all the literature by yourself, independent of your background on GANs.
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ArticlesConditional Image GenerationImage GenerationImage-to-Image TranslationVideo GenerationVideo-to-Video SynthesisSimilar Papers 제목 키워드 기반
GANs-NQM: A Generative Adversarial Networks based No Reference Quality Assessment Metric for RGB-D Synthesized Views
In this paper, we proposed a no-reference (NR) quality metric for RGB plus image-depth (RGB-D) synthesis images based on Generative Adversarial Networks (GANs), namely GANs-NQM. Due to the failure of the inpainting on di…
Local DistortionVisual Word Selection without Re-Coding and Re-Pooling
The Bag-of-Words (BoW) representation is widely used in computer vision. The size of the codebook impacts the time and space complexity of the applications that use BoW. Thus, given a training set for a particular comput…
GAN2Shape - Create 3D Shape with 2D GANs
In this blog post, we discuss the key points of the paper “Do 2D GANs Know 3D Shape? Unsupervised 3D Shape Reconstruction from 2D Image GANs” (GAN2Shape) by Pan et al. We will discuss both the theory and code (in the aut…
3D Shape ReconstructionTransformer-based Generative Adversarial Networks in Computer Vision: A Comprehensive Survey
Generative Adversarial Networks (GANs) have been very successful for synthesizing the images in a given dataset. The artificially generated images by GANs are very realistic. The GANs have shown potential usability in se…
Image GenerationImage-to-Image TranslationTranslationGenerative Adversarial Networks in Computer Vision: A Survey and Taxonomy
Generative adversarial networks (GANs) have been extensively studied in the past few years. Arguably their most significant impact has been in the area of computer vision where great advances have been made in challenges…
AttributeImage GenerationImage InpaintingImage Super-Resolution+2