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"Generative adversarial networks (GANs): Introduction, Taxonomy, Varian" by Preeti Sharma, Manoj Kumar et al.

"Generative adversarial networks (GANs): Introduction, Taxonomy, Varian" by Preeti Sharma, Manoj Kumar et al.
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Generative Adversarial Networks Gans , Generative Adversarial Networks , Inception Score , Inception Distance ,

"A GAN-Based Model of Deepfake Detection in Social Media" by Preeti, Manoj Kumar et al.

DeepFake uses Generative+Adversarial Network for successfully switching the identities of two people. Large public databases and deep learning methods are now rapidly available because of the proliferation of easily accessible tools online. It has resulted in the emergence of very real appealing fake content that produced a bad impact and challenges for society to deal. Pre-trained generative adversarial networks (GANs) that can flawlessly substitute one person's face in a video or image for that other are proving supportive for implementing deepfake. This paper primarily presented a study of methods used to implement deepfake. Also, discuss the main deepfake's manipulation and detection techniques, and the implementation and detection of deepfake using Deep Convolution-based GAN models. A study of Comparative analyses of proposed GAN with other exiting GAN models using parameters Inception Score "IS"and Fréchet Inception Distance "FID"is also embedded. A ....

Adversarial Network , Deep Convolution Based , Inception Score , Inception Distance , Deep Fakes , Deep Learning , Digital Forensics , Ace Manipulation , Face Recognition , Generative Adversarial Network , Image Vision , Media Forensics ,

"Multi-SelfGAN: A Self-Guiding Neural Architecture Search Method for Ge" by Jiachen Shi, Guoqiang Zhou et al.

In recent years, Reinforcement Learning and Gradient optimization were applied with Neural Architecture Search algorithms in Generative Adversarial Network to achieve their state-of-the-art (SOTA) performance. However, the existing RL-based methods utilised the calculation of Inception Score or Fréchet Inception Distance as the reward value to guide the controller, which actually wasted much of searching time. In order to improve the search efficiency without degradation of performance, this paper proposes recycling the discriminator to evaluate the performance of architectures, in other words, we propose to self-guide the search process. In the mean time, we introduce new concept of multiple controllers and the method of reward shaping to independently and effectively search the cell architectures. The experiments demonstrate the effectiveness and efficiency of our Multi-Self GAN and the ablation study also exhibits its robustness. ....

Generative Adversarial Network , Reinforcement Learning , Neural Architecture Search , Inception Score , Inception Distance , Computer Architecture , Generative Adversarial Network , Generative Adversarial Networks , Neural Architecture Search , Reinforcement Learning , Eward Shaping , Earch Problems ,