Ss-cpgan: self-supervised cut-and-pasting generative adversarial network for object segmentation

HIGHLIGHTS

• who: Firstname Lastname and colleagues from the School of Computer Science, FEIT, University of Technology Sydney, Sydney, NSW, Australia have published the paper: SS-CPGAN: Self-Supervised Cut-and-Pasting Generative Adversarial Network for Object Segmentation, in the Journal: Sensors 2023, 23, 3649. of /2023/
• what: The experiments demonstrate that the proposed method significantly outperforms the state-of-the-art methods on the standard benchmark datasets. The aim of GANs is to generate diverse, high-quality images while also ensuring the stability of GAN training . To maintain an enriched real data representation and improve the . . .
  

   

  

  If you want to have access to all the content you need to log in!

  Thanks :)

  Username or E-mail:

  If you don't have an account, you can create one here.