Human Sketch Recognition using Generative Adversarial Networks and One-Shot Learning

Wadhwa, Deepanshu; Maharana, Utkarsh; Shah, Devina; Yadav, Vaibhav; Pandey, Prashant

doi:10.1109/ic3.2019.8844885

Cited by 2 publications

(1 citation statement)

References 27 publications

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“…The draft recognition design developed with this study gives positive results in multiple data sets. The distinction in recognition correctness compared to the original images is seen as three percent [57].…”

Section: Work With Generative Adversarial Networkmentioning

confidence: 99%

Generative Networks and Royalty-Free Products

Özkan

Erdoğmuş

2020

Sakarya University Journal of Computer and Information Sciences

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In recent years, with the increasing power of computers and Graphics Processing Units (GPUs), vast variety of deep neural networks architectures have been created and realized. One of the most interesting and generative type of the networks are Generative Adversarial Networks (GANs). GANs are used to create things such as music, images or a film scenerio. GANs consist of two networks working simultaneously. Generative network captures data distribution and discriminative network estimates the probability of the Generative Network output, coming from training data of discriminative network. The objective is to both maximizing the generative network products reality and minimize the discriminative network classification error. This procedure is a minimax two-player game. In this paper, it has been aimed to review the latest studies with GANs, to gather the recent studies in an article and to discuss the possible issues with royalty free products created by GANs. With this aim, from 2018 to today, the studies on GANs have been gathered to the citation numbers. As a result, the recent studies with GANs have been summarized and the potential issues related to GANs have been submitted.

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Section: Work With Generative Adversarial Networkmentioning

confidence: 99%