A survey on GANs for computer vision: Recent research, analysis and taxonomy

• URL: http://arxiv.org/abs/2203.11242v3
• Date: Fri, 16 Feb 2024 12:48:21 GMT
• Title: A survey on GANs for computer vision: Recent research, analysis and taxonomy
• Authors: Guillermo Iglesias, Edgar Talavera and Alberto D\'iaz-\'Alvarez
• Abstract summary: Survey aims to provide an overview of GANs, showing the latest architectures, optimizations of the loss functions, validation metrics and application areas. The efficiency of the different variants of the model architecture will be evaluated, as well as showing the best application area. The final objective of this survey is to provide a summary of the evolution and performance of the GANs which are having better results to guide future researchers in the field.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the last few years, there have been several revolutions in the field of deep learning, mainly headlined by the large impact of Generative Adversarial Networks (GANs). GANs not only provide an unique architecture when defining their models, but also generate incredible results which have had a direct impact on society. Due to the significant improvements and new areas of research that GANs have brought, the community is constantly coming up with new researches that make it almost impossible to keep up with the times. Our survey aims to provide a general overview of GANs, showing the latest architectures, optimizations of the loss functions, validation metrics and application areas of the most widely recognized variants. The efficiency of the different variants of the model architecture will be evaluated, as well as showing the best application area; as a vital part of the process, the different metrics for evaluating the performance of GANs and the frequently used loss functions will be analyzed. The final objective of this survey is to provide a summary of the evolution and performance of the GANs which are having better results to guide future researchers in the field.

Related papers

• Leveraging the Domain Adaptation of Retrieval Augmented Generation Models for Question Answering and Reducing Hallucination [3.879723687090678]
  Retrieval Augmented Generation Model stands out to be highly effective on downstream applications like Question Answering. Recently, RAG-end2end model further optimized the architecture and achieved notable performance improvements on domain adaptation. In this paper, we investigated the performance of diverse RAG and RAG-like architectures through domain adaptation.
  arXiv  Detail & Related papers  (2024-10-23T11:32:46Z)
• On the Convergence of (Stochastic) Gradient Descent for Kolmogorov--Arnold Networks [56.78271181959529]
  Kolmogorov--Arnold Networks (KANs) have gained significant attention in the deep learning community. Empirical investigations demonstrate that KANs optimized via gradient descent (SGD) are capable of achieving near-zero training loss.
  arXiv  Detail & Related papers  (2024-10-10T15:34:10Z)
• Data-Centric Long-Tailed Image Recognition [49.90107582624604]
  Long-tail models exhibit a strong demand for high-quality data. Data-centric approaches aim to enhance both the quantity and quality of data to improve model performance. There is currently a lack of research into the underlying mechanisms explaining the effectiveness of information augmentation.
  arXiv  Detail & Related papers  (2023-11-03T06:34:37Z)
• A Chronological Survey of Theoretical Advancements in Generative Adversarial Networks for Computer Vision [0.0]
  Generative Adversarial Networks (GANs) have been workhorse generative models for last many years, especially in the research field of computer vision. There have been many surveys on GANs, organizing the vast GAN literature from various focus and perspectives. This survey intends to bridge that gap and present some of the landmark research works on the theory and application of GANs, in chronological order.
  arXiv  Detail & Related papers  (2023-11-02T05:11:47Z)
• Ten Years of Generative Adversarial Nets (GANs): A survey of the state-of-the-art [0.0]
  Generative Adversarial Networks (GANs) have rapidly emerged as powerful tools for generating realistic and diverse data across various domains. In February 2018, GAN secured the leading spot on the Top Ten Global Breakthrough Technologies List'' This survey aims to provide a general overview of GANs, summarizing the latent architecture, validation metrics, and application areas of the most widely recognized variants.
  arXiv  Detail & Related papers  (2023-08-30T20:46:45Z)
• Data Augmentation techniques in time series domain: A survey and taxonomy [0.20971479389679332]
  Deep neural networks used to work with time series heavily depend on the size and consistency of the datasets used in training. This work systematically reviews the current state-of-the-art in the area to provide an overview of all available algorithms. The ultimate aim of this study is to provide a summary of the evolution and performance of areas that produce better results to guide future researchers in this field.
  arXiv  Detail & Related papers  (2022-06-25T17:09:00Z)
• Generative Adversarial Networks for Image Super-Resolution: A Survey [101.39605080291783]
  Single image super-resolution (SISR) has played an important role in the field of image processing. Recent generative adversarial networks (GANs) can achieve excellent results on low-resolution images with small samples. In this paper, we conduct a comparative study of GANs from different perspectives.
  arXiv  Detail & Related papers  (2022-04-28T16:35:04Z)
• Fine-Grained Image Analysis with Deep Learning: A Survey [146.22351342315233]
  Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer vision and pattern recognition. This paper attempts to re-define and broaden the field of FGIA by consolidating two fundamental fine-grained research areas -- fine-grained image recognition and fine-grained image retrieval.
  arXiv  Detail & Related papers  (2021-11-11T09:43:56Z)
• Generative adversarial networks in time series: A survey and taxonomy [7.885673762715387]
  Generative adversarial networks (GANs) studies have grown exponentially in the past few years. GAN applications have diversified across disciplines such as time series and sequence generation. As a relatively new niche for GANs, fieldwork is ongoing to develop high quality, diverse and private time series data.
  arXiv  Detail & Related papers  (2021-07-23T09:38:51Z)
• Domain Generalization: A Survey [146.68420112164577]
  Domain generalization (DG) aims to achieve OOD generalization by only using source domain data for model learning. For the first time, a comprehensive literature review is provided to summarize the ten-year development in DG.
  arXiv  Detail & Related papers  (2021-03-03T16:12:22Z)
• Rethinking Generalization of Neural Models: A Named Entity Recognition Case Study [81.11161697133095]
  We take the NER task as a testbed to analyze the generalization behavior of existing models from different perspectives. Experiments with in-depth analyses diagnose the bottleneck of existing neural NER models. As a by-product of this paper, we have open-sourced a project that involves a comprehensive summary of recent NER papers.
  arXiv  Detail & Related papers  (2020-01-12T04:33:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.