Abstract
We present a method that enhances the RATGAN using the idea of CycleGAN to prevent the model from collapsing. The generator with the proposed recurrent affine transformation for text-to-image synthesis and the fusion blocks are connected by an RNN during the generation of fake images to assure the global assignment of text information. The transformation from the phony images to the text is then accomplished using a CNN and encoder with LSTM. A comparison of the generated text with the original text ensures the generator’s preciseness. In order to achieve the state of the art, we evaluated both unconditional and conditional generation’s effectiveness in relation to the accepted standards.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
