Video Deblurring by Fitting to Test Data

Ren, Xuanchi; Qian, Zian; Chen, Qifeng

doi:10.48550/arxiv.2012.05228

Cited by 1 publication

(1 citation statement)

References 32 publications

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“…When a video is generated, the tracking of some objects can be difficult, generating fuzziness in some portions of the image. Some works have tried to tackle this problem [180,173,126], but it is still an open problem.…”

Section: Video Generationmentioning

confidence: 99%

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

Iglesias¹,

Talavera²,

Díaz-Álvarez³

2022

Preprint

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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.

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Section: Video Generationmentioning

confidence: 99%