A survey on generative adversarial networks and their variants methods

Data science becomes creative with generative adversarial networks (GANs) which have had a big success since they were introduced in 2014 by Ian J. Goodfellow and co-authors. In technical term the GANs are based on the unsupervised learning of two artificial neural networks called Generator and Discriminator both trained under the adversarial learning idea. The major goal of GAN is to generate new samples that estimate the potential distribution of real data. Due to its huge success, many modified versions have been proposed in the last two years. We summarize in this review paper GAN’s background, architecture and its application fields. Then, we discuss the different extensions of GAN over the original model and provide a comparative analysis of these techniques.

[1]  Mourad Zaied,et al.  Unsupervised Features Extraction Using a Multi-view Self Organizing Map for Image Classification , 2017, 2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA).

[3]  Y. Benayed,et al.  Wavelet Networks for phonemes Recognition , 2009 .

[4]  Jan Kautz,et al.  MoCoGAN: Decomposing Motion and Content for Video Generation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[5]  Chokri Ben Amar,et al.  Fast beta wavelet network-based feature extraction for image copy detection , 2016, Neurocomputing.

[6]  G. Mariem,et al.  Detection of Abnormal Movements of a Crowd in a Video Scene , 2022 .

[7]  Dimitris N. Metaxas,et al.  StackGAN: Text to Photo-Realistic Image Synthesis with Stacked Generative Adversarial Networks , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).

[8]  Soumith Chintala,et al.  Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.

[9]  Bernt Schiele,et al.  Generative Adversarial Text to Image Synthesis , 2016, ICML.

[10]  Antonio Torralba,et al.  Generating Videos with Scene Dynamics , 2016, NIPS.

[11]  Fisher Yu,et al.  Scribbler: Controlling Deep Image Synthesis with Sketch and Color , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Chokri Ben Amar,et al.  Beta wavelets. Synthesis and application to lossy image compression , 2005, Adv. Eng. Softw..

[13]  Kun Xu,et al.  A survey of image synthesis and editing with generative adversarial networks , 2017 .

[14]  Christian Ledig,et al.  Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[16]  Chokri Ben Amar,et al.  Deep learning with shallow architecture for image classification , 2015, 2015 International Conference on High Performance Computing & Simulation (HPCS).

[17]  Chokri Ben Amar,et al.  Multi-input Multi-output Beta Wavelet Network: Modeling of Acoustic Units for Speech Recognition , 2012, ArXiv.

[18]  Mourad Zaied,et al.  Detection and Classification of Dental Caries in X-ray Images Using Deep Neural Networks , 2016, ICSEA 2016.

[19]  Chokri Ben Amar,et al.  Face recognition based on Beta 2D Elastic Bunch Graph Matching , 2013, 13th International Conference on Hybrid Intelligent Systems (HIS 2013).

[20]  Pieter Abbeel,et al.  InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets , 2016, NIPS.

[21]  Simon Osindero,et al.  Conditional Generative Adversarial Nets , 2014, ArXiv.

[22]  Bernt Schiele,et al.  Learning What and Where to Draw , 2016, NIPS.

[23]  Alexei A. Efros,et al.  Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).