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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 gansGenerative adversarial networksInception scoreInception distance

Decoding The Evaluation Process Of Generative AI Companies By Venture Capitalists

These companies, leveraging cutting-edge technology, are driving innovation across industries such as healthcare, finance, and entertainment.

Autoencoders vaesGenerative adversarial networks gansTechnology stackEuropean unionArtificial intelligenceVariational autoencodersGenerative adversarial networksDeep reinforcementHigher inception scoresInception distancePath lengthGeneral data protection regulationProperty rightsFrechet 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 networkDeep convolution basedInception scoreInception distanceDeep fakesDeep learningDigital forensicsAce manipulationFace recognitionGenerative adversarial networkImage visionMedia forensics

Practical Deep Learning for Coders - Part 2 overview

Practical Deep Learning for Coders - Part 2 overview
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Perceptrons mlpsGlorot xavierGroup normConvolutional neural networks cnnsStable diffusionHugging facePractical deep learningDenoising diffusion probabilistic modelsDenoising diffusion implicit modelsMulti layer perceptronsLayer normalizationDiffusion probabilistic modelsDiffusion implicit modelLayer perceptronNlearning techniquesNeural networks

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 networkReinforcement learningNeural architecture searchInception scoreInception distanceComputer architectureGenerative adversarial networkGenerative adversarial networksNeural architecture searchReinforcement learningEward shapingEarch problems