Towards the Applications of Generative Adversarial Networks Beyond Images

Recently, uses of Generative Adversarial Networks (GANs) have seen tremendous advancements. The technique of GAN has been effectively utilized for the amalgamation of high-fidelity realistic images and augmentation of data, also refining image compressions, and much more. From imitating genuine expressions to discovering deep space and from linking the human-machine feeling division to introducing new art forms, GANs have shielded almost all arenas. GANSynth is the technology that is being explored for synthetization of sound waves. This study mainly focuses on the application of GAN in real-world problems and an overview of GANs, their variants, and potential applications in discrete domains and also discusses the benefits, shortcomings, and significant challenges to the practical application of GAN in various fields, including Audio and Video. The thought to ponder for researchers working in a similar field is the combination of applications of GAN in Images and Audio for its further diverse application in video synthesizing. The paper also discusses advancements of GAN applications in Audio processing besides Images and further suggests its applications in Video as well.

[1]  Manolya Kavakli-Thorne,et al.  Applications of Generative Adversarial Networks (GANs): An Updated Review , 2019, Archives of Computational Methods in Engineering.

[2]  Jeff Donahue,et al.  Large Scale GAN Training for High Fidelity Natural Image Synthesis , 2018, ICLR.

[3]  Jan Kautz,et al.  High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[4]  Jaakko Lehtinen,et al.  Progressive Growing of GANs for Improved Quality, Stability, and Variation , 2017, ICLR.

[5]  Yingtao Tian,et al.  Towards the Automatic Anime Characters Creation with Generative Adversarial Networks , 2017, ArXiv.

[6]  Luc Van Gool,et al.  Pose Guided Person Image Generation , 2017, NIPS.

[7]  Ran He,et al.  Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[8]  Ali Farhadi,et al.  SeGAN: Segmenting and Generating the Invisible , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[9]  Jean-Luc Dugelay,et al.  Face aging with conditional generative adversarial networks , 2017, 2017 IEEE International Conference on Image Processing (ICIP).

[10]  Alexei A. Efros,et al.  Image-to-Image Translation with Conditional Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  Bogdan Raducanu,et al.  Invertible Conditional GANs for image editing , 2016, ArXiv.

[12]  Lior Wolf,et al.  Unsupervised Cross-Domain Image Generation , 2016, ICLR.

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

[14]  Alexei A. Efros,et al.  Context Encoders: Feature Learning by Inpainting , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

[16]  Michael Jones,et al.  An improved deep learning architecture for person re-identification , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[17]  Geoffrey E. Hinton,et al.  A Learning Algorithm for Boltzmann Machines , 1985, Cogn. Sci..